complex problem solving courses

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Solving Complex Problems: Structured Thinking, Design Principles, and AI

Sang-Gook Kim

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How do you solve important, large-scale challenges with evolving and contradictory constraints? In this 5-day course, transform your approach to large-scale problem solving, from multi-stakeholder engineering projects to the online spread of misinformation. Alongside engineers and leaders from diverse industries, you’ll explore actionable innovative frameworks for assessing, communicating, and implementing complex systems—and significantly increase your likelihood of success.

THIS COURSE MAY BE TAKEN INDIVIDUALLY OR AS PART OF THE  PROFESSIONAL CERTIFICATE PROGRAM IN INNOVATION & TECHNOLOGY  OR THE  PROFESSIONAL CERTIFICATE PROGRAM IN DESIGN & MANUFACTURING .

Engineering projects with shifting goals. Inefficient national healthcare systems. The online spread of misinformation. Every day, professionals are tasked with addressing major challenges that present opportunities for great triumph—or significant failure. How do you approach an important, large-scale challenge with evolving and contradictory constraints? Is the solution a new technology, a new policy, or something else altogether? In our new course Solving Complex Problems: Structured Thinking, Design Principles, and AI , you’ll acquire core principles that will change the way you approach and solve large-scale challenges—increasing your likelihood of success. Over the course of five days, you will explore proven design principles, heuristic-based insights, and problem-solving approaches, and learn how to persuasively present concepts and system architectures to stakeholders. Methods utilize recent developments in AI and Big Data, as well as innovative strategies from MIT Lincoln Laboratory that have been successfully applied to large and complex national defense systems. By taking part in interactive lectures and hands-on projects, you will learn to think through and leverage important steps, including problem abstraction, idea generation, concept development and refinement, system-level thinking, and proposal generation. Alongside an accomplished group of global peers, you will explore the strategies and frameworks you need to implement large-scale systems that can have a significant positive impact—and minimize the probability of failure.

Certificate of Completion from MIT Professional Education  

• Approach and solve large and complex problems.
• Assess end-to-end processes and associated challenges, in order to significantly increase the likelihood of success in developing more complex systems.
• Implement effective problem-solving techniques, including abstracting the problem, idea generation, concept development and refinement, system-level thinking, and proposal generation.
• Utilize system-level thinking skills to evaluate, refine, down select, and evaluate best ideas and concepts.
• Apply the Axiomatic Design methodology to a broad range of applications in manufacturing, product design, software, and architecture.
• Generate and present proposals that clearly articulate innovative ideas, clarify the limits of current strategies, define potential customers and impact, and outline a success-oriented system development and risk mitigation plan.
• Effectively communicate ideas and persuade others, and provide valuable feedback.
• Confidently develop and execute large-scale system concepts that will drive significant positive impact.

Edwin F. David Head of the Engineering Division, MIT Lincoln Laboratory

Jonathan E. Gans Group Leader of the Systems and Architectures Group, MIT Lincoln Laboratory

Robert T-I. Shin Principal Staff in the Intelligence, Surveillance, and Reconnaissance (ISR) and Tactical Systems Division, MIT Lincoln Laboratory Director, MIT Beaver Works

This course is appropriate for professionals who design or manage complex systems with shifting needs and goals. It is also well suited to those who want to improve the quality and performance of their operations and decision-making in a large-scale system environment. Potential participants include engineers, group leaders, and senior managers in government and industries including automotive, aerospace, semiconductors, engineering, manufacturing, healthcare, bio-medical, finance, architecture, public policy, education, and military.

Computer Requirements

A laptop with PowerPoint is required.

• Courses for Individuals

Understanding and Solving Complex Business Problems

Management and Leadership

Certificate Credits

- Operations

- Systems Thinking

• Participants

Course Highlights

• Discover MIT's unique, powerful, and integrative System Dynamics approach to assess problems that will not go away
• Experience the Beer Game, which simulates the supply chain of the beer industry
• Learn a new way of thinking about and resolving complex, persistent problems that emerge from change
• Earn a certificate of course completion from the MIT Sloan School of Management

Why attend Understanding and Solving Complex Business Problems?

Systems thinking was designed to improve people's ability to manage organizations comprehensively in a volatile global environment. It offers managers a framework for understanding complex situations and the dynamics those situations produce. Systems thinking is a response to the rapid changes in technology, population, and economic activity that are transforming the world, and as a way to deal with the ever-increasing complexity of today's business.

Senior managers can use systems thinking to design policies that lead their organizations to high performance. The program is intended to give participants the tools and confidence to manage organizations with full understanding and solid strategy.

Course experience

This complex problem-solving course introduces participants to MIT's unique, powerful, and integrative System Dynamics approach to assess problems that will not go away and to produce the results they want. Through exercises and simulation models, participants experience the long-term side effects and impacts of decisions and understand the ways in which performance is tied to structures and policies.

Sample Schedule—Subject to Change

This program is designed for executives with decision-making responsibility who are looking for fresh ideas to resolve organizational problems.

Past participants have included

• VPs and EVPs
• Corporate planners and strategists
• Senior Project Managers
• Product Development Managers

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This program is designed to empower you to analyze complex problems in any area by using powerful yet very simple tools which are also very easy to use in real world, I enjoyed it a lot.

—Jia X.

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What mit’s beer game teaches about panic hoarding, mit’s ‘beer game’ shows humans are weakest link in supply chains, the beer game, john sterman on system dynamics, how the ‘beer game’ helps retailers solve toilet paper crisis, course offerings.

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Creative Thinking: Innovative Solutions to Complex Challenges

Learn how to grow a culture of creativity to innovate competitive solutions.

All Start Dates

8:30 AM – 4:30 PM ET

2 consecutive days

Registration Deadline

May 28, 2024

October 8, 2024

Overview: Creative Thinking Skills Course

The tech breakthrough that makes smartphones irrelevant, a new viral ad campaign, your company’s next big revenue generator — ideas like these could be sitting in your brain; all you need are the creative thinking skills and strategies to pull them out.

This interactive program focuses explicitly on the creative thinking skills you need to solve complex problems and design innovative solutions. Learn how to transform your thinking from the standard “why can’t we” to the powerful “how might we.” Crack the code on how to consistently leverage your team’s creative potential in order to drive innovation within your organization. Explore how to build a climate for innovation, remove barriers to creativity, cultivate courage, and create more agile, proactive, and inspired teams.

You will leave this program with new ideas about how to think more productively and how to introduce creative thinking skills into your organization. You can apply key takeaways immediately to implement a new leadership vision, inspire renewed enthusiasm, and enjoy the skills and tools to tackle challenges and seize opportunities.

Innovation experts Anne Manning and Susan Robertson bring to this highly-interactive and powerful program their decades of experience promoting corporate innovation, teaching the art of creative problem solving, and applying the principles of brain science to solve complex challenges.

Who Should Take Creative Thinking Skills Training?

This program is ideal for leaders with at least 3 years of management experience. It is designed for leaders who want to develop new strategies, frameworks, and tools for creative problem solving. Whether you are a team lead, project manager, sales director, or executive, you’ll learn powerful tools to lead your team and your organization to create innovative solutions to complex challenges.

All participants will earn a Certificate of Participation from the Harvard Division of Continuing Education.

Benefits of Creative Thinking Skills Training

The goal of this creative thinking program is to help you develop the strategic concepts and tactical skills to lead creative problem solving for your team and your organization. You will learn to:

• Retrain your brain to avoid negative cognitive biases and long-held beliefs and myths that sabotage creative problem solving and innovation
• Become a more nimble, proactive, and inspired thinker and leader
• Create the type of organizational culture that supports collaboration and nurtures rather than kills ideas
• Gain a practical toolkit for solving the “unsolvable” by incorporating creative thinking into day-to-day processes
• Understand cognitive preferences (yours and others’) to adapt the creative thinking process and drive your team’s success
• Develop techniques that promote effective brainstorming and enable you to reframe problems in a way that inspires innovative solutions

The curriculum in this highly interactive program utilizes research-based methodologies and techniques to build creative thinking skills and stimulate creative problem solving.

Through intensive group discussions and small-group exercises, you will focus on topics such as:

• The Creative Problem Solving process: a researched, learnable, repeatable process for uncovering new and useful ideas. This process includes a “how to” on clarifying, ideating, developing, and implementing new solutions to intractable problems
• The cognitive preferences that drive how we approach problems, and how to leverage those cognitive preferences for individual and team success
• How to develop—and implement— a methodology that overcomes barriers to innovative thinking and fosters the generation of new ideas, strategies, and techniques
• The role of language, including asking the right questions, in reframing problems, challenging assumptions, and driving successful creative problem solving
• Fostering a culture that values, nurtures, and rewards creative solutions

Considering this program?

Send yourself the details.

Related Programs

• Design Thinking: Creating Better Customer Experiences
• Agile Leadership: Transforming Mindsets and Capabilities in Your Organization

June Schedule

• Creative Challenges: A Team Sport
• The Place to Begin: Reframe the Challenge
• Ideas on Demand
• Building a Creative Organization

October Schedule

Instructors, anne manning, susan robertson, certificates of leadership excellence.

The Certificates of Leadership Excellence (CLE) are designed for leaders with the desire to enhance their business acumen, challenge current thinking, and expand their leadership skills.

This program is one of several CLE qualifying programs. Register today and get started earning your certificate.

Harvard Division of Continuing Education

The Division of Continuing Education (DCE) at Harvard University is dedicated to bringing rigorous academics and innovative teaching capabilities to those seeking to improve their lives through education. We make Harvard education accessible to lifelong learners from high school to retirement.

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Creative Thinking for Complex Problem Solving

The challenges businesses face today are increasingly complex and systemic, often resisting obvious and definitive solutions. This complexity is frequently met with oversimplification, over-analysis, and quick fixes. But complex problem solving requires unconventional thinking to make unexpected connections—connections that others might not see. You can create these connections by bringing play and rigor into your problem-solving process. The most effective problem solvers harness creative thinking to see problems from unique angles, experiment with new and innovative ideas, and maintain momentum throughout the problem-solving process to make measured progress and move from problems to possibilities. Launching March 2024, our newest course will help you become a dynamic problem solver, equipped to take on today’s most intricate challenges with creative thinking and confidence.

Course Outcomes

• Look at problems through different perspectives to open up many possibilities.
• Refine your instincts into actionable and innovative solutions.
• Learn how to de-risk and experiment to build resilient strategies.
• Balance creative thinking and rigor to get to breakthrough ideas and sustainable solutions.

Skills You’ll Gain

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What You'll Learn

Introduction: welcoming complexity, watch a sneak peek, 2 video lessons.

Welcome Complexity: An Introduction to Mindsets and Methods—Delve into the essential components of curiosity, experimentation, and iteration to welcome complexity as an opportunity.

1 Assignment

Articulate a Complex Problem: In your organization, reflect on how play and rigor show up.

2 Discussions

When have you seen the power of adding more imagination or creativity into addressing a complex problem? What was the impact?

What common complex problem-solving pitfall tends to happen most on your team: oversimplifying, overanalyzing, or quick fixes? Why and how could you counter it?

2 Resources

Mindsets that Drive Complex Problem Solving: This guide provides information on embracing the mindsets of exploration, empathetic curiosity, and experimentation.

Overcoming Common Pitfalls: Strategies to recognize and address common pitfalls such as oversimplification, overanalysis, and premature solution finding.

Week 1: Open Up the Problem With Curiosity

4 video lessons.

Expand the Question: Engage Stakeholders and Invite Fresh Perspectives—Learn to uncover and ask the right questions by involving diverse stakeholders

Build Empathy: Put Humans at the Center—Apply critical thinking strategies to understand the biases and needs of stakeholders using three IDEO case studies

Diverge and Converge: Generate Possibilities and Make Choices—Explore IDEO’s diverge/converge process, and the powerful role ambiguity plays in problem solving

The Science of Play: Why Creative Problem Solving Works—Explore the neuroscience behind imagination and play, and why these concepts are so vital in problem-solving spaces.

Refine Your Problem Statement: Reflect on and apply techniques to deconstruct assumptions, broaden perspectives, refine your central problem statement based on human needs and resources.

3 Discussions

What “sacred myths” are present in your organization? How might they limit creativity and innovation?

Does your organization oversimplify, overanalyze, or jump to solutions when facing complexity? Why?

How can leaders nurture acceptance of uncertainty in the innovation process?

Uncover Assumptions: Tools to help you uncover starting points, hunches, and strong beliefs about your problem.

Right-Size the Question: Learn how to sharpen your problem statement with lessons from IDEO case studies.

Week 2: Get Tangible Through Experimentation

Level up Ideas—Techniques to evolve early hunches into tangible concepts

Build confidence—Learn to assess concepts using IDEO’s Desirability, Viability, and Feasibility framework

De-risk Through Experimentation—Learn how to use prototyping to de-risk your solutions

The Art of Observation—Techniques for capturing unbiased observations from your experiments

Create Prototypes: Bring your solutions to life with rapid prototyping, uncover hidden assumptions, and build resilience in your solutions.

What technique(s) helped you most in leveling up early ideas into testable concepts?

How might you increase the diversity of perspectives involved in shaping and assessing early prototypes?

In what ways can leaders nurture acceptance of uncertainty and nonlinearity in the early innovation process?

Tools for Prototyping and Experimentation: Guides on co-creation sessions, mock pitches, and boundary concepts.

Simulating Strategies and Solutions: Learn how to use strategy board games as tools for fostering problem-solving, creativity, and innovation.

Week 3: Iterate As You Learn

3 video lessons.

Meaning Making: Identify Patterns and Themes Through Synthesis—Balance playful synthesis with rigorous analysis to build compelling narratives

Pivot and Iterate—Techniques to adapt and evolve future solutions

Learn from The Future—Use future scenarios to pressure-test ideas and adapt to evolving concepts

Uncover Deep Insights: Apply the techniques of affinity clustering, stakeholder critiques, and working backward from future visioning to derive meaningful insights and identify moments to iterate or pivot.

What metrics would indicate you are making meaningful progress amidst complexity and uncertainty?

What insights challenged your assumptions about this problem space or audience?

In what ways can experiments that “fail” still provide value in complexity?

Find the Implications from Insights: Strategies for leveraging insights in problem-solving.

Measure Progress: Methods to track progress and align with future scenarios.

Conclusion: Maintain Momentum

1 video lesson.

Sustain Commitment—Learn how to inspire behavioral change and sustain commitment.

Reflect on the Mindsets and Methods to Drive Sustained Change: Determine everyday rituals that motivate teams and counter change fatigue. Adopt lenses assessing current strategies while envisioning aspirational futures.

Why is it important to define success by outcomes rather than only concrete outputs/deliverables? How might this shape your approach?

What everyday rituals can leaders employ to keep teams inspired and committed for the long haul of complex problem-solving?

Temperature Check: Evaluate your progress and strategize the next steps to enhance confidence in your problem-solving direction.

Meet Your Instructors

Kate Schnippering

Executive design director at ideo.

Kate Schnippering is an Executive Design Director at IDEO, with a focus on creative technology. Kate brings ‘build to learn' experimentation to make real the futures we imagine. She creates conditions for teams and partners to immerse in imagination as a collective act—uplifting dreams and rigor in equal measure. In nearly a decade at IDEO, Kate’s developed teams, leaders, and organizations.

Her work investigates pathways to positive, systemic change for people and nature—by harnessing expressive technologies to make science & data relatable, and grow the power of everyday people. She’s built a real-world ‘magic school bus’ that teaches rover engineering to middle schoolers on Mars, designed a product for patients to partner directly with medical researchers in the study of rare diseases, and guided a youth mental health platform from proof of concept to delivery.

Michelle Lee

Partner and executive managing director at ideo play lab.

Michelle Lee is a Partner and Managing Director at IDEO, where she has applied her passion for play to leading interdisciplinary teams of designers and researchers in bringing engaging, interactive, and playful experiences to market. She believes in leveraging the principles of play to connect with people on a deeper emotional level that captivates, delights, and empowers.

Through her work, she has helped clients enhance workplace culture, championed responsible digital design, inspired underrepresented students to pursue careers in STEM, and supported organizations as they adopted practices in line with a circular economy. Michelle has shared her passion for play at SXSW, The Delight Conference, The Culture Summit, Circularity 23 and through numerous podcasts and articles.

Frequently Asked Questions

How do ideo u cohort courses work does my time zone matter.

We offer three types of courses: self-paced courses, cohort courses, and certificate programs. Cohort courses run on a set calendar, with fixed start and end dates. Course learning is self-paced within those dates and requires approximately 4-5 hours per week over 5 weeks. Courses consist of videos, activities, assignments, access to course teaching teams, and feedback from a global community of learners. There are also optional 1-hour video Community Conversations, held weekly by the teaching team. 

All of our cohort courses are fully online, so you can take them from any time zone, anywhere in the world. With our cohort course experience , while you'll be learning alongside other learners, you'll still have the flexibility to work at the pace that fits your own schedule. There aren’t mandatory live components, so you don't have to worry about having to log in at a specific time. At the same time, you'll have access to a teaching team, which is composed of experts in the field who are there to provide you feedback, and there are also plenty of options to connect with your fellow learners.

What is the role of the instructor and teaching team? Will learners be able to get feedback?

Course instructors have a strong presence in the courses through the course videos, but they're not actively providing feedback or holding direct conversations with our learners. We have a teaching team to ensure that you have the feedback, guidance, and support you need to learn successfully in your course. Our teaching team members are design practitioners that have experience applying course methods and mindsets in a wide variety of contexts around the world.

Our teaching team consists of teaching leads and teaching assistants, who are experts in their fields. Many of them have been with IDEO U for many years, and we have selected those who have direct experience with applying the course methods and mindsets in all sorts of contexts around the world. They all go through multiple training sessions by our instructional designers on not only on the subject matter, but also on how to create safe and collaborative learning experiences and environments.

What are Community Conversations, and how are they related to the course material?

Community Conversations are one-hour live video conversations hosted by the teaching team on Zoom. These happen once per week, with each one having two to three time options to accommodate different time zones. Each week focuses on the lesson that you’ve just gone through, so the output and the content depend on the specific lessons. You'll have the opportunity if you work together with your peers on the tools and mindsets from the course, reflect on what you’ve learned, and also address any challenges that you might be going through.

What will I have access to during and after my course?

All course materials, including videos, activities, and assignments will be available while you are enrolled in a course. During the 5 weeks of the course, you will have full access to our learning platform and can refer back to it any time. You will only have access to the course materials while you are enrolled. 

Assignments must be submitted during the 5-week course duration in order for you to receive a certificate of completion.

Can I take the course with my team?

Absolutely! We have had many teams go through our courses together. For those taking our courses as a team, we provide a number of additional benefits:

1. A Team Learning Guide, developed to provide your team with resources to facilitate offline discussions that complement the in-course experience.

2. A Manage Learners function, which provides visibility into your team's progress within the course.

3. The ability to create a private Learning Circle, which is a closed space for discussion on the learning platform specifically for your team.

For more information, visit our Team Learning page.

Do you offer discounts?

We offer a discount when you enroll in multiple courses at the same time through some of our certificate programs, including Foundations in Design Thinking , Business Innovation , Human-Centered Strategy , and Communicating for Impact . 

You can also enter your email address at the bottom of this page in order to receive updates on future offers or possible discounts. 

Will I get a certificate after completing a course?

After completing a cohort course, you will be able to add it to your “licenses and certifications” on LinkedIn.

We also have certificate programs that consist of multiple courses. After completing a certificate, you will receive a certificate of completion via email as a downloadable PDF within 1-2 weeks of completing the final required course. Certificates are configured for uploading and sharing on LinkedIn.

How do I purchase a cohort course?

You can purchase a course on our website using a credit card, PayPal, or Shop Pay. For US customers, we also offer installment plans at checkout if you use the Shop Pay method of payment.

We typically are not able to accommodate bank transfer or invoicing. However, if your order includes 10 seats or more, please contact [email protected] and our team will be happy to review your request. 

Collaborate with a Global Community

Work with expert coaches.

Our teaching team has extensive applied industry knowledge. They'll help deepen your understanding and application of the course content by facilitating written discussions, live video moments, and assignment feedback.

Expand Your Network

Join virtual live discussion groups for deeper conversation, reflection, and connection led by teaching team members and available multiple times a week across time zones.

Receive Feedback

Gain tips, techniques, and a downloadable feedback guide; and share and receive feedback on assignments from peers.

Loved by Learners Across the Globe

“Michelle has a passion for thinking BIG, addressing complexity with playful creativity, and somehow making it all fun! She understands deeply the importance and implications of play across contexts, industries, and solutions - and uses it masterfully in her own work and in helping others come up with solutions and innovations. I would 100% choose her as my teacher and mentor in this space every time - and have!”

"Kate and her team brought people together from across the Ranger Business to engage in complex strategy development through a playful and curious program of work. With prototypes and ideas in hand, we explored new places and met new people, growing and learning together as a team. These glimpses into the future continue to inspire us, have changed our approach to work and compel us to continuously adjust and refine our Ranger strategy to support future generations."

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Enroll As a Team

The practice and application of design thinking, innovation, and creativity is highly collaborative and team based—which is why we believe that learning is better together. Take a course as a team and develop new skills and mindsets, have deeper discussion during course kickoff and debrief sessions, and build a shared understanding.

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Creative Thinking: Innovative Solutions to Complex Challenges

Learn how to grow a culture of creativity to innovate competitive solutions.

Associated Schools

Harvard Division of Continuing Education

Professional & Executive Development

Course description.

Leverage your team's creativity to solve complex problems and innovate. Learn how to facilitate creative problem-solving, cultivate courage, inspire teams, and build a climate for innovation. 

Instructors

Anne Manning

Susan Robertson

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35 problem-solving techniques and methods for solving complex problems

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All teams and organizations encounter challenges as they grow. There are problems that might occur for teams when it comes to miscommunication or resolving business-critical issues . You may face challenges around growth , design , user engagement, and even team culture and happiness. In short, problem-solving techniques should be part of every team’s skillset.

Problem-solving methods are primarily designed to help a group or team through a process of first identifying problems and challenges , ideating possible solutions , and then evaluating the most suitable .

Finding effective solutions to complex problems isn’t easy, but by using the right process and techniques, you can help your team be more efficient in the process.

So how do you develop strategies that are engaging, and empower your team to solve problems effectively?

In this blog post, we share a series of problem-solving tools you can use in your next workshop or team meeting. You’ll also find some tips for facilitating the process and how to enable others to solve complex problems.

Let’s get started! 

How do you identify problems?

How do you identify the right solution.

• Tips for more effective problem-solving

Complete problem-solving methods

• Problem-solving techniques to identify and analyze problems
• Problem-solving techniques for developing solutions

Problem-solving warm-up activities

Closing activities for a problem-solving process.

Before you can move towards finding the right solution for a given problem, you first need to identify and define the problem you wish to solve. 

Here, you want to clearly articulate what the problem is and allow your group to do the same. Remember that everyone in a group is likely to have differing perspectives and alignment is necessary in order to help the group move forward. 

Identifying a problem accurately also requires that all members of a group are able to contribute their views in an open and safe manner. It can be scary for people to stand up and contribute, especially if the problems or challenges are emotive or personal in nature. Be sure to try and create a psychologically safe space for these kinds of discussions.

Remember that problem analysis and further discussion are also important. Not taking the time to fully analyze and discuss a challenge can result in the development of solutions that are not fit for purpose or do not address the underlying issue.

Successfully identifying and then analyzing a problem means facilitating a group through activities designed to help them clearly and honestly articulate their thoughts and produce usable insight.

With this data, you might then produce a problem statement that clearly describes the problem you wish to be addressed and also state the goal of any process you undertake to tackle this issue.  

Finding solutions is the end goal of any process. Complex organizational challenges can only be solved with an appropriate solution but discovering them requires using the right problem-solving tool.

After you’ve explored a problem and discussed ideas, you need to help a team discuss and choose the right solution. Consensus tools and methods such as those below help a group explore possible solutions before then voting for the best. They’re a great way to tap into the collective intelligence of the group for great results!

Remember that the process is often iterative. Great problem solvers often roadtest a viable solution in a measured way to see what works too. While you might not get the right solution on your first try, the methods below help teams land on the most likely to succeed solution while also holding space for improvement.

Every effective problem solving process begins with an agenda . A well-structured workshop is one of the best methods for successfully guiding a group from exploring a problem to implementing a solution.

In SessionLab, it’s easy to go from an idea to a complete agenda . Start by dragging and dropping your core problem solving activities into place . Add timings, breaks and necessary materials before sharing your agenda with your colleagues.

The resulting agenda will be your guide to an effective and productive problem solving session that will also help you stay organized on the day!

Tips for more effective problem solving

Problem-solving activities are only one part of the puzzle. While a great method can help unlock your team’s ability to solve problems, without a thoughtful approach and strong facilitation the solutions may not be fit for purpose.

Let’s take a look at some problem-solving tips you can apply to any process to help it be a success!

Clearly define the problem

Jumping straight to solutions can be tempting, though without first clearly articulating a problem, the solution might not be the right one. Many of the problem-solving activities below include sections where the problem is explored and clearly defined before moving on.

This is a vital part of the problem-solving process and taking the time to fully define an issue can save time and effort later. A clear definition helps identify irrelevant information and it also ensures that your team sets off on the right track.

Don’t jump to conclusions

It’s easy for groups to exhibit cognitive bias or have preconceived ideas about both problems and potential solutions. Be sure to back up any problem statements or potential solutions with facts, research, and adequate forethought.

The best techniques ask participants to be methodical and challenge preconceived notions. Make sure you give the group enough time and space to collect relevant information and consider the problem in a new way. By approaching the process with a clear, rational mindset, you’ll often find that better solutions are more forthcoming.  

Try different approaches  

Problems come in all shapes and sizes and so too should the methods you use to solve them. If you find that one approach isn’t yielding results and your team isn’t finding different solutions, try mixing it up. You’ll be surprised at how using a new creative activity can unblock your team and generate great solutions.

Don’t take it personally 

Depending on the nature of your team or organizational problems, it’s easy for conversations to get heated. While it’s good for participants to be engaged in the discussions, ensure that emotions don’t run too high and that blame isn’t thrown around while finding solutions.

You’re all in it together, and even if your team or area is seeing problems, that isn’t necessarily a disparagement of you personally. Using facilitation skills to manage group dynamics is one effective method of helping conversations be more constructive.

Get the right people in the room

Your problem-solving method is often only as effective as the group using it. Getting the right people on the job and managing the number of people present is important too!

If the group is too small, you may not get enough different perspectives to effectively solve a problem. If the group is too large, you can go round and round during the ideation stages.

Creating the right group makeup is also important in ensuring you have the necessary expertise and skillset to both identify and follow up on potential solutions. Carefully consider who to include at each stage to help ensure your problem-solving method is followed and positioned for success.

Document everything

The best solutions can take refinement, iteration, and reflection to come out. Get into a habit of documenting your process in order to keep all the learnings from the session and to allow ideas to mature and develop. Many of the methods below involve the creation of documents or shared resources. Be sure to keep and share these so everyone can benefit from the work done!

Bring a facilitator 

Facilitation is all about making group processes easier. With a subject as potentially emotive and important as problem-solving, having an impartial third party in the form of a facilitator can make all the difference in finding great solutions and keeping the process moving. Consider bringing a facilitator to your problem-solving session to get better results and generate meaningful solutions!

Develop your problem-solving skills

It takes time and practice to be an effective problem solver. While some roles or participants might more naturally gravitate towards problem-solving, it can take development and planning to help everyone create better solutions.

You might develop a training program, run a problem-solving workshop or simply ask your team to practice using the techniques below. Check out our post on problem-solving skills to see how you and your group can develop the right mental process and be more resilient to issues too!

Design a great agenda

Workshops are a great format for solving problems. With the right approach, you can focus a group and help them find the solutions to their own problems. But designing a process can be time-consuming and finding the right activities can be difficult.

Check out our workshop planning guide to level-up your agenda design and start running more effective workshops. Need inspiration? Check out templates designed by expert facilitators to help you kickstart your process!

In this section, we’ll look at in-depth problem-solving methods that provide a complete end-to-end process for developing effective solutions. These will help guide your team from the discovery and definition of a problem through to delivering the right solution.

If you’re looking for an all-encompassing method or problem-solving model, these processes are a great place to start. They’ll ask your team to challenge preconceived ideas and adopt a mindset for solving problems more effectively.

• Six Thinking Hats
• Lightning Decision Jam
• Problem Definition Process
• Discovery & Action Dialogue

Design Sprint 2.0

• Open Space Technology

1. Six Thinking Hats

Individual approaches to solving a problem can be very different based on what team or role an individual holds. It can be easy for existing biases or perspectives to find their way into the mix, or for internal politics to direct a conversation.

Six Thinking Hats is a classic method for identifying the problems that need to be solved and enables your team to consider them from different angles, whether that is by focusing on facts and data, creative solutions, or by considering why a particular solution might not work.

Like all problem-solving frameworks, Six Thinking Hats is effective at helping teams remove roadblocks from a conversation or discussion and come to terms with all the aspects necessary to solve complex problems.

2. Lightning Decision Jam

Featured courtesy of Jonathan Courtney of AJ&Smart Berlin, Lightning Decision Jam is one of those strategies that should be in every facilitation toolbox. Exploring problems and finding solutions is often creative in nature, though as with any creative process, there is the potential to lose focus and get lost.

Unstructured discussions might get you there in the end, but it’s much more effective to use a method that creates a clear process and team focus.

In Lightning Decision Jam, participants are invited to begin by writing challenges, concerns, or mistakes on post-its without discussing them before then being invited by the moderator to present them to the group.

From there, the team vote on which problems to solve and are guided through steps that will allow them to reframe those problems, create solutions and then decide what to execute on. 

By deciding the problems that need to be solved as a team before moving on, this group process is great for ensuring the whole team is aligned and can take ownership over the next stages. 

Lightning Decision Jam (LDJ)   #action   #decision making   #problem solving   #issue analysis   #innovation   #design   #remote-friendly   The problem with anything that requires creative thinking is that it’s easy to get lost—lose focus and fall into the trap of having useless, open-ended, unstructured discussions. Here’s the most effective solution I’ve found: Replace all open, unstructured discussion with a clear process. What to use this exercise for: Anything which requires a group of people to make decisions, solve problems or discuss challenges. It’s always good to frame an LDJ session with a broad topic, here are some examples: The conversion flow of our checkout Our internal design process How we organise events Keeping up with our competition Improving sales flow

3. Problem Definition Process

While problems can be complex, the problem-solving methods you use to identify and solve those problems can often be simple in design. 

By taking the time to truly identify and define a problem before asking the group to reframe the challenge as an opportunity, this method is a great way to enable change.

Begin by identifying a focus question and exploring the ways in which it manifests before splitting into five teams who will each consider the problem using a different method: escape, reversal, exaggeration, distortion or wishful. Teams develop a problem objective and create ideas in line with their method before then feeding them back to the group.

This method is great for enabling in-depth discussions while also creating space for finding creative solutions too!

Problem Definition   #problem solving   #idea generation   #creativity   #online   #remote-friendly   A problem solving technique to define a problem, challenge or opportunity and to generate ideas.

4. The 5 Whys 

Sometimes, a group needs to go further with their strategies and analyze the root cause at the heart of organizational issues. An RCA or root cause analysis is the process of identifying what is at the heart of business problems or recurring challenges. 

The 5 Whys is a simple and effective method of helping a group go find the root cause of any problem or challenge and conduct analysis that will deliver results. 

By beginning with the creation of a problem statement and going through five stages to refine it, The 5 Whys provides everything you need to truly discover the cause of an issue.

The 5 Whys   #hyperisland   #innovation   This simple and powerful method is useful for getting to the core of a problem or challenge. As the title suggests, the group defines a problems, then asks the question “why” five times, often using the resulting explanation as a starting point for creative problem solving.

5. World Cafe

World Cafe is a simple but powerful facilitation technique to help bigger groups to focus their energy and attention on solving complex problems.

World Cafe enables this approach by creating a relaxed atmosphere where participants are able to self-organize and explore topics relevant and important to them which are themed around a central problem-solving purpose. Create the right atmosphere by modeling your space after a cafe and after guiding the group through the method, let them take the lead!

Making problem-solving a part of your organization’s culture in the long term can be a difficult undertaking. More approachable formats like World Cafe can be especially effective in bringing people unfamiliar with workshops into the fold. 

World Cafe   #hyperisland   #innovation   #issue analysis   World Café is a simple yet powerful method, originated by Juanita Brown, for enabling meaningful conversations driven completely by participants and the topics that are relevant and important to them. Facilitators create a cafe-style space and provide simple guidelines. Participants then self-organize and explore a set of relevant topics or questions for conversation.

6. Discovery & Action Dialogue (DAD)

One of the best approaches is to create a safe space for a group to share and discover practices and behaviors that can help them find their own solutions.

With DAD, you can help a group choose which problems they wish to solve and which approaches they will take to do so. It’s great at helping remove resistance to change and can help get buy-in at every level too!

This process of enabling frontline ownership is great in ensuring follow-through and is one of the methods you will want in your toolbox as a facilitator.

Discovery & Action Dialogue (DAD)   #idea generation   #liberating structures   #action   #issue analysis   #remote-friendly   DADs make it easy for a group or community to discover practices and behaviors that enable some individuals (without access to special resources and facing the same constraints) to find better solutions than their peers to common problems. These are called positive deviant (PD) behaviors and practices. DADs make it possible for people in the group, unit, or community to discover by themselves these PD practices. DADs also create favorable conditions for stimulating participants’ creativity in spaces where they can feel safe to invent new and more effective practices. Resistance to change evaporates as participants are unleashed to choose freely which practices they will adopt or try and which problems they will tackle. DADs make it possible to achieve frontline ownership of solutions.

7. Design Sprint 2.0

Want to see how a team can solve big problems and move forward with prototyping and testing solutions in a few days? The Design Sprint 2.0 template from Jake Knapp, author of Sprint, is a complete agenda for a with proven results.

Developing the right agenda can involve difficult but necessary planning. Ensuring all the correct steps are followed can also be stressful or time-consuming depending on your level of experience.

Use this complete 4-day workshop template if you are finding there is no obvious solution to your challenge and want to focus your team around a specific problem that might require a shortcut to launching a minimum viable product or waiting for the organization-wide implementation of a solution.

8. Open space technology

Open space technology- developed by Harrison Owen – creates a space where large groups are invited to take ownership of their problem solving and lead individual sessions. Open space technology is a great format when you have a great deal of expertise and insight in the room and want to allow for different takes and approaches on a particular theme or problem you need to be solved.

Start by bringing your participants together to align around a central theme and focus their efforts. Explain the ground rules to help guide the problem-solving process and then invite members to identify any issue connecting to the central theme that they are interested in and are prepared to take responsibility for.

Once participants have decided on their approach to the core theme, they write their issue on a piece of paper, announce it to the group, pick a session time and place, and post the paper on the wall. As the wall fills up with sessions, the group is then invited to join the sessions that interest them the most and which they can contribute to, then you’re ready to begin!

Everyone joins the problem-solving group they’ve signed up to, record the discussion and if appropriate, findings can then be shared with the rest of the group afterward.

Open Space Technology   #action plan   #idea generation   #problem solving   #issue analysis   #large group   #online   #remote-friendly   Open Space is a methodology for large groups to create their agenda discerning important topics for discussion, suitable for conferences, community gatherings and whole system facilitation

Techniques to identify and analyze problems

Using a problem-solving method to help a team identify and analyze a problem can be a quick and effective addition to any workshop or meeting.

While further actions are always necessary, you can generate momentum and alignment easily, and these activities are a great place to get started.

We’ve put together this list of techniques to help you and your team with problem identification, analysis, and discussion that sets the foundation for developing effective solutions.

Let’s take a look!

• The Creativity Dice
• Fishbone Analysis
• Problem Tree
• SWOT Analysis
• Agreement-Certainty Matrix
• The Journalistic Six
• LEGO Challenge
• What, So What, Now What?
• Journalists

Individual and group perspectives are incredibly important, but what happens if people are set in their minds and need a change of perspective in order to approach a problem more effectively?

Flip It is a method we love because it is both simple to understand and run, and allows groups to understand how their perspectives and biases are formed. 

Participants in Flip It are first invited to consider concerns, issues, or problems from a perspective of fear and write them on a flip chart. Then, the group is asked to consider those same issues from a perspective of hope and flip their understanding.  

No problem and solution is free from existing bias and by changing perspectives with Flip It, you can then develop a problem solving model quickly and effectively.

Flip It!   #gamestorming   #problem solving   #action   Often, a change in a problem or situation comes simply from a change in our perspectives. Flip It! is a quick game designed to show players that perspectives are made, not born.

10. The Creativity Dice

One of the most useful problem solving skills you can teach your team is of approaching challenges with creativity, flexibility, and openness. Games like The Creativity Dice allow teams to overcome the potential hurdle of too much linear thinking and approach the process with a sense of fun and speed. 

In The Creativity Dice, participants are organized around a topic and roll a dice to determine what they will work on for a period of 3 minutes at a time. They might roll a 3 and work on investigating factual information on the chosen topic. They might roll a 1 and work on identifying the specific goals, standards, or criteria for the session.

Encouraging rapid work and iteration while asking participants to be flexible are great skills to cultivate. Having a stage for idea incubation in this game is also important. Moments of pause can help ensure the ideas that are put forward are the most suitable. 

The Creativity Dice   #creativity   #problem solving   #thiagi   #issue analysis   Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.

11. Fishbone Analysis

Organizational or team challenges are rarely simple, and it’s important to remember that one problem can be an indication of something that goes deeper and may require further consideration to be solved.

Fishbone Analysis helps groups to dig deeper and understand the origins of a problem. It’s a great example of a root cause analysis method that is simple for everyone on a team to get their head around. 

Participants in this activity are asked to annotate a diagram of a fish, first adding the problem or issue to be worked on at the head of a fish before then brainstorming the root causes of the problem and adding them as bones on the fish. 

Using abstractions such as a diagram of a fish can really help a team break out of their regular thinking and develop a creative approach.

Fishbone Analysis   #problem solving   ##root cause analysis   #decision making   #online facilitation   A process to help identify and understand the origins of problems, issues or observations.

12. Problem Tree 

Encouraging visual thinking can be an essential part of many strategies. By simply reframing and clarifying problems, a group can move towards developing a problem solving model that works for them. 

In Problem Tree, groups are asked to first brainstorm a list of problems – these can be design problems, team problems or larger business problems – and then organize them into a hierarchy. The hierarchy could be from most important to least important or abstract to practical, though the key thing with problem solving games that involve this aspect is that your group has some way of managing and sorting all the issues that are raised.

Once you have a list of problems that need to be solved and have organized them accordingly, you’re then well-positioned for the next problem solving steps.

Problem tree   #define intentions   #create   #design   #issue analysis   A problem tree is a tool to clarify the hierarchy of problems addressed by the team within a design project; it represents high level problems or related sublevel problems.

13. SWOT Analysis

Chances are you’ve heard of the SWOT Analysis before. This problem-solving method focuses on identifying strengths, weaknesses, opportunities, and threats is a tried and tested method for both individuals and teams.

Start by creating a desired end state or outcome and bare this in mind – any process solving model is made more effective by knowing what you are moving towards. Create a quadrant made up of the four categories of a SWOT analysis and ask participants to generate ideas based on each of those quadrants.

Once you have those ideas assembled in their quadrants, cluster them together based on their affinity with other ideas. These clusters are then used to facilitate group conversations and move things forward. 

SWOT analysis   #gamestorming   #problem solving   #action   #meeting facilitation   The SWOT Analysis is a long-standing technique of looking at what we have, with respect to the desired end state, as well as what we could improve on. It gives us an opportunity to gauge approaching opportunities and dangers, and assess the seriousness of the conditions that affect our future. When we understand those conditions, we can influence what comes next.

14. Agreement-Certainty Matrix

Not every problem-solving approach is right for every challenge, and deciding on the right method for the challenge at hand is a key part of being an effective team.

The Agreement Certainty matrix helps teams align on the nature of the challenges facing them. By sorting problems from simple to chaotic, your team can understand what methods are suitable for each problem and what they can do to ensure effective results. 

If you are already using Liberating Structures techniques as part of your problem-solving strategy, the Agreement-Certainty Matrix can be an invaluable addition to your process. We’ve found it particularly if you are having issues with recurring problems in your organization and want to go deeper in understanding the root cause. 

Agreement-Certainty Matrix   #issue analysis   #liberating structures   #problem solving   You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic .  A problem is simple when it can be solved reliably with practices that are easy to duplicate.  It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably.  A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail.  Chaotic is when the context is too turbulent to identify a path forward.  A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.”  The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.

Organizing and charting a team’s progress can be important in ensuring its success. SQUID (Sequential Question and Insight Diagram) is a great model that allows a team to effectively switch between giving questions and answers and develop the skills they need to stay on track throughout the process. 

Begin with two different colored sticky notes – one for questions and one for answers – and with your central topic (the head of the squid) on the board. Ask the group to first come up with a series of questions connected to their best guess of how to approach the topic. Ask the group to come up with answers to those questions, fix them to the board and connect them with a line. After some discussion, go back to question mode by responding to the generated answers or other points on the board.

It’s rewarding to see a diagram grow throughout the exercise, and a completed SQUID can provide a visual resource for future effort and as an example for other teams.

SQUID   #gamestorming   #project planning   #issue analysis   #problem solving   When exploring an information space, it’s important for a group to know where they are at any given time. By using SQUID, a group charts out the territory as they go and can navigate accordingly. SQUID stands for Sequential Question and Insight Diagram.

16. Speed Boat

To continue with our nautical theme, Speed Boat is a short and sweet activity that can help a team quickly identify what employees, clients or service users might have a problem with and analyze what might be standing in the way of achieving a solution.

Methods that allow for a group to make observations, have insights and obtain those eureka moments quickly are invaluable when trying to solve complex problems.

In Speed Boat, the approach is to first consider what anchors and challenges might be holding an organization (or boat) back. Bonus points if you are able to identify any sharks in the water and develop ideas that can also deal with competitors!   

Speed Boat   #gamestorming   #problem solving   #action   Speedboat is a short and sweet way to identify what your employees or clients don’t like about your product/service or what’s standing in the way of a desired goal.

17. The Journalistic Six

Some of the most effective ways of solving problems is by encouraging teams to be more inclusive and diverse in their thinking.

Based on the six key questions journalism students are taught to answer in articles and news stories, The Journalistic Six helps create teams to see the whole picture. By using who, what, when, where, why, and how to facilitate the conversation and encourage creative thinking, your team can make sure that the problem identification and problem analysis stages of the are covered exhaustively and thoughtfully. Reporter’s notebook and dictaphone optional.

The Journalistic Six – Who What When Where Why How   #idea generation   #issue analysis   #problem solving   #online   #creative thinking   #remote-friendly   A questioning method for generating, explaining, investigating ideas.

18. LEGO Challenge

Now for an activity that is a little out of the (toy) box. LEGO Serious Play is a facilitation methodology that can be used to improve creative thinking and problem-solving skills. 

The LEGO Challenge includes giving each member of the team an assignment that is hidden from the rest of the group while they create a structure without speaking.

What the LEGO challenge brings to the table is a fun working example of working with stakeholders who might not be on the same page to solve problems. Also, it’s LEGO! Who doesn’t love LEGO! 

LEGO Challenge   #hyperisland   #team   A team-building activity in which groups must work together to build a structure out of LEGO, but each individual has a secret “assignment” which makes the collaborative process more challenging. It emphasizes group communication, leadership dynamics, conflict, cooperation, patience and problem solving strategy.

19. What, So What, Now What?

If not carefully managed, the problem identification and problem analysis stages of the problem-solving process can actually create more problems and misunderstandings.

The What, So What, Now What? problem-solving activity is designed to help collect insights and move forward while also eliminating the possibility of disagreement when it comes to identifying, clarifying, and analyzing organizational or work problems. 

Facilitation is all about bringing groups together so that might work on a shared goal and the best problem-solving strategies ensure that teams are aligned in purpose, if not initially in opinion or insight.

Throughout the three steps of this game, you give everyone on a team to reflect on a problem by asking what happened, why it is important, and what actions should then be taken. 

This can be a great activity for bringing our individual perceptions about a problem or challenge and contextualizing it in a larger group setting. This is one of the most important problem-solving skills you can bring to your organization.

W³ – What, So What, Now What?   #issue analysis   #innovation   #liberating structures   You can help groups reflect on a shared experience in a way that builds understanding and spurs coordinated action while avoiding unproductive conflict. It is possible for every voice to be heard while simultaneously sifting for insights and shaping new direction. Progressing in stages makes this practical—from collecting facts about What Happened to making sense of these facts with So What and finally to what actions logically follow with Now What . The shared progression eliminates most of the misunderstandings that otherwise fuel disagreements about what to do. Voila!

20. Journalists  

Problem analysis can be one of the most important and decisive stages of all problem-solving tools. Sometimes, a team can become bogged down in the details and are unable to move forward.

Journalists is an activity that can avoid a group from getting stuck in the problem identification or problem analysis stages of the process.

In Journalists, the group is invited to draft the front page of a fictional newspaper and figure out what stories deserve to be on the cover and what headlines those stories will have. By reframing how your problems and challenges are approached, you can help a team move productively through the process and be better prepared for the steps to follow.

Journalists   #vision   #big picture   #issue analysis   #remote-friendly   This is an exercise to use when the group gets stuck in details and struggles to see the big picture. Also good for defining a vision.

Problem-solving techniques for developing solutions 

The success of any problem-solving process can be measured by the solutions it produces. After you’ve defined the issue, explored existing ideas, and ideated, it’s time to narrow down to the correct solution.

Use these problem-solving techniques when you want to help your team find consensus, compare possible solutions, and move towards taking action on a particular problem.

• Improved Solutions
• Four-Step Sketch
• 15% Solutions
• How-Now-Wow matrix
• Impact Effort Matrix

21. Mindspin  

Brainstorming is part of the bread and butter of the problem-solving process and all problem-solving strategies benefit from getting ideas out and challenging a team to generate solutions quickly. 

With Mindspin, participants are encouraged not only to generate ideas but to do so under time constraints and by slamming down cards and passing them on. By doing multiple rounds, your team can begin with a free generation of possible solutions before moving on to developing those solutions and encouraging further ideation. 

This is one of our favorite problem-solving activities and can be great for keeping the energy up throughout the workshop. Remember the importance of helping people become engaged in the process – energizing problem-solving techniques like Mindspin can help ensure your team stays engaged and happy, even when the problems they’re coming together to solve are complex. 

MindSpin   #teampedia   #idea generation   #problem solving   #action   A fast and loud method to enhance brainstorming within a team. Since this activity has more than round ideas that are repetitive can be ruled out leaving more creative and innovative answers to the challenge.

22. Improved Solutions

After a team has successfully identified a problem and come up with a few solutions, it can be tempting to call the work of the problem-solving process complete. That said, the first solution is not necessarily the best, and by including a further review and reflection activity into your problem-solving model, you can ensure your group reaches the best possible result. 

One of a number of problem-solving games from Thiagi Group, Improved Solutions helps you go the extra mile and develop suggested solutions with close consideration and peer review. By supporting the discussion of several problems at once and by shifting team roles throughout, this problem-solving technique is a dynamic way of finding the best solution. 

Improved Solutions   #creativity   #thiagi   #problem solving   #action   #team   You can improve any solution by objectively reviewing its strengths and weaknesses and making suitable adjustments. In this creativity framegame, you improve the solutions to several problems. To maintain objective detachment, you deal with a different problem during each of six rounds and assume different roles (problem owner, consultant, basher, booster, enhancer, and evaluator) during each round. At the conclusion of the activity, each player ends up with two solutions to her problem.

23. Four Step Sketch

Creative thinking and visual ideation does not need to be confined to the opening stages of your problem-solving strategies. Exercises that include sketching and prototyping on paper can be effective at the solution finding and development stage of the process, and can be great for keeping a team engaged. 

By going from simple notes to a crazy 8s round that involves rapidly sketching 8 variations on their ideas before then producing a final solution sketch, the group is able to iterate quickly and visually. Problem-solving techniques like Four-Step Sketch are great if you have a group of different thinkers and want to change things up from a more textual or discussion-based approach.

Four-Step Sketch   #design sprint   #innovation   #idea generation   #remote-friendly   The four-step sketch is an exercise that helps people to create well-formed concepts through a structured process that includes: Review key information Start design work on paper,  Consider multiple variations , Create a detailed solution . This exercise is preceded by a set of other activities allowing the group to clarify the challenge they want to solve. See how the Four Step Sketch exercise fits into a Design Sprint

24. 15% Solutions

Some problems are simpler than others and with the right problem-solving activities, you can empower people to take immediate actions that can help create organizational change. 

Part of the liberating structures toolkit, 15% solutions is a problem-solving technique that focuses on finding and implementing solutions quickly. A process of iterating and making small changes quickly can help generate momentum and an appetite for solving complex problems.

Problem-solving strategies can live and die on whether people are onboard. Getting some quick wins is a great way of getting people behind the process.   

It can be extremely empowering for a team to realize that problem-solving techniques can be deployed quickly and easily and delineate between things they can positively impact and those things they cannot change. 

15% Solutions   #action   #liberating structures   #remote-friendly   You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference.  15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change.  With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.

25. How-Now-Wow Matrix

The problem-solving process is often creative, as complex problems usually require a change of thinking and creative response in order to find the best solutions. While it’s common for the first stages to encourage creative thinking, groups can often gravitate to familiar solutions when it comes to the end of the process. 

When selecting solutions, you don’t want to lose your creative energy! The How-Now-Wow Matrix from Gamestorming is a great problem-solving activity that enables a group to stay creative and think out of the box when it comes to selecting the right solution for a given problem.

Problem-solving techniques that encourage creative thinking and the ideation and selection of new solutions can be the most effective in organisational change. Give the How-Now-Wow Matrix a go, and not just for how pleasant it is to say out loud. 

How-Now-Wow Matrix   #gamestorming   #idea generation   #remote-friendly   When people want to develop new ideas, they most often think out of the box in the brainstorming or divergent phase. However, when it comes to convergence, people often end up picking ideas that are most familiar to them. This is called a ‘creative paradox’ or a ‘creadox’. The How-Now-Wow matrix is an idea selection tool that breaks the creadox by forcing people to weigh each idea on 2 parameters.

26. Impact and Effort Matrix

All problem-solving techniques hope to not only find solutions to a given problem or challenge but to find the best solution. When it comes to finding a solution, groups are invited to put on their decision-making hats and really think about how a proposed idea would work in practice. 

The Impact and Effort Matrix is one of the problem-solving techniques that fall into this camp, empowering participants to first generate ideas and then categorize them into a 2×2 matrix based on impact and effort.

Activities that invite critical thinking while remaining simple are invaluable. Use the Impact and Effort Matrix to move from ideation and towards evaluating potential solutions before then committing to them. 

Impact and Effort Matrix   #gamestorming   #decision making   #action   #remote-friendly   In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.

27. Dotmocracy

If you’ve followed each of the problem-solving steps with your group successfully, you should move towards the end of your process with heaps of possible solutions developed with a specific problem in mind. But how do you help a group go from ideation to putting a solution into action? 

Dotmocracy – or Dot Voting -is a tried and tested method of helping a team in the problem-solving process make decisions and put actions in place with a degree of oversight and consensus. 

One of the problem-solving techniques that should be in every facilitator’s toolbox, Dot Voting is fast and effective and can help identify the most popular and best solutions and help bring a group to a decision effectively. 

Dotmocracy   #action   #decision making   #group prioritization   #hyperisland   #remote-friendly   Dotmocracy is a simple method for group prioritization or decision-making. It is not an activity on its own, but a method to use in processes where prioritization or decision-making is the aim. The method supports a group to quickly see which options are most popular or relevant. The options or ideas are written on post-its and stuck up on a wall for the whole group to see. Each person votes for the options they think are the strongest, and that information is used to inform a decision.

All facilitators know that warm-ups and icebreakers are useful for any workshop or group process. Problem-solving workshops are no different.

Use these problem-solving techniques to warm up a group and prepare them for the rest of the process. Activating your group by tapping into some of the top problem-solving skills can be one of the best ways to see great outcomes from your session.

• Check-in/Check-out
• Doodling Together
• Show and Tell
• Constellations
• Draw a Tree

28. Check-in / Check-out

Solid processes are planned from beginning to end, and the best facilitators know that setting the tone and establishing a safe, open environment can be integral to a successful problem-solving process.

Check-in / Check-out is a great way to begin and/or bookend a problem-solving workshop. Checking in to a session emphasizes that everyone will be seen, heard, and expected to contribute. 

If you are running a series of meetings, setting a consistent pattern of checking in and checking out can really help your team get into a groove. We recommend this opening-closing activity for small to medium-sized groups though it can work with large groups if they’re disciplined!

Check-in / Check-out   #team   #opening   #closing   #hyperisland   #remote-friendly   Either checking-in or checking-out is a simple way for a team to open or close a process, symbolically and in a collaborative way. Checking-in/out invites each member in a group to be present, seen and heard, and to express a reflection or a feeling. Checking-in emphasizes presence, focus and group commitment; checking-out emphasizes reflection and symbolic closure.

29. Doodling Together  

Thinking creatively and not being afraid to make suggestions are important problem-solving skills for any group or team, and warming up by encouraging these behaviors is a great way to start. 

Doodling Together is one of our favorite creative ice breaker games – it’s quick, effective, and fun and can make all following problem-solving steps easier by encouraging a group to collaborate visually. By passing cards and adding additional items as they go, the workshop group gets into a groove of co-creation and idea development that is crucial to finding solutions to problems. 

Doodling Together   #collaboration   #creativity   #teamwork   #fun   #team   #visual methods   #energiser   #icebreaker   #remote-friendly   Create wild, weird and often funny postcards together & establish a group’s creative confidence.

30. Show and Tell

You might remember some version of Show and Tell from being a kid in school and it’s a great problem-solving activity to kick off a session.

Asking participants to prepare a little something before a workshop by bringing an object for show and tell can help them warm up before the session has even begun! Games that include a physical object can also help encourage early engagement before moving onto more big-picture thinking.

By asking your participants to tell stories about why they chose to bring a particular item to the group, you can help teams see things from new perspectives and see both differences and similarities in the way they approach a topic. Great groundwork for approaching a problem-solving process as a team! 

Show and Tell   #gamestorming   #action   #opening   #meeting facilitation   Show and Tell taps into the power of metaphors to reveal players’ underlying assumptions and associations around a topic The aim of the game is to get a deeper understanding of stakeholders’ perspectives on anything—a new project, an organizational restructuring, a shift in the company’s vision or team dynamic.

31. Constellations

Who doesn’t love stars? Constellations is a great warm-up activity for any workshop as it gets people up off their feet, energized, and ready to engage in new ways with established topics. It’s also great for showing existing beliefs, biases, and patterns that can come into play as part of your session.

Using warm-up games that help build trust and connection while also allowing for non-verbal responses can be great for easing people into the problem-solving process and encouraging engagement from everyone in the group. Constellations is great in large spaces that allow for movement and is definitely a practical exercise to allow the group to see patterns that are otherwise invisible. 

Constellations   #trust   #connection   #opening   #coaching   #patterns   #system   Individuals express their response to a statement or idea by standing closer or further from a central object. Used with teams to reveal system, hidden patterns, perspectives.

32. Draw a Tree

Problem-solving games that help raise group awareness through a central, unifying metaphor can be effective ways to warm-up a group in any problem-solving model.

Draw a Tree is a simple warm-up activity you can use in any group and which can provide a quick jolt of energy. Start by asking your participants to draw a tree in just 45 seconds – they can choose whether it will be abstract or realistic. 

Once the timer is up, ask the group how many people included the roots of the tree and use this as a means to discuss how we can ignore important parts of any system simply because they are not visible.

All problem-solving strategies are made more effective by thinking of problems critically and by exposing things that may not normally come to light. Warm-up games like Draw a Tree are great in that they quickly demonstrate some key problem-solving skills in an accessible and effective way.

Draw a Tree   #thiagi   #opening   #perspectives   #remote-friendly   With this game you can raise awarness about being more mindful, and aware of the environment we live in.

Each step of the problem-solving workshop benefits from an intelligent deployment of activities, games, and techniques. Bringing your session to an effective close helps ensure that solutions are followed through on and that you also celebrate what has been achieved.

Here are some problem-solving activities you can use to effectively close a workshop or meeting and ensure the great work you’ve done can continue afterward.

• One Breath Feedback
• Who What When Matrix
• Response Cards

How do I conclude a problem-solving process?

All good things must come to an end. With the bulk of the work done, it can be tempting to conclude your workshop swiftly and without a moment to debrief and align. This can be problematic in that it doesn’t allow your team to fully process the results or reflect on the process.

At the end of an effective session, your team will have gone through a process that, while productive, can be exhausting. It’s important to give your group a moment to take a breath, ensure that they are clear on future actions, and provide short feedback before leaving the space. 

The primary purpose of any problem-solving method is to generate solutions and then implement them. Be sure to take the opportunity to ensure everyone is aligned and ready to effectively implement the solutions you produced in the workshop.

Remember that every process can be improved and by giving a short moment to collect feedback in the session, you can further refine your problem-solving methods and see further success in the future too.

33. One Breath Feedback

Maintaining attention and focus during the closing stages of a problem-solving workshop can be tricky and so being concise when giving feedback can be important. It’s easy to incur “death by feedback” should some team members go on for too long sharing their perspectives in a quick feedback round. 

One Breath Feedback is a great closing activity for workshops. You give everyone an opportunity to provide feedback on what they’ve done but only in the space of a single breath. This keeps feedback short and to the point and means that everyone is encouraged to provide the most important piece of feedback to them. 

One breath feedback   #closing   #feedback   #action   This is a feedback round in just one breath that excels in maintaining attention: each participants is able to speak during just one breath … for most people that’s around 20 to 25 seconds … unless of course you’ve been a deep sea diver in which case you’ll be able to do it for longer.

34. Who What When Matrix 

Matrices feature as part of many effective problem-solving strategies and with good reason. They are easily recognizable, simple to use, and generate results.

The Who What When Matrix is a great tool to use when closing your problem-solving session by attributing a who, what and when to the actions and solutions you have decided upon. The resulting matrix is a simple, easy-to-follow way of ensuring your team can move forward. 

Great solutions can’t be enacted without action and ownership. Your problem-solving process should include a stage for allocating tasks to individuals or teams and creating a realistic timeframe for those solutions to be implemented or checked out. Use this method to keep the solution implementation process clear and simple for all involved. 

Who/What/When Matrix   #gamestorming   #action   #project planning   With Who/What/When matrix, you can connect people with clear actions they have defined and have committed to.

35. Response cards

Group discussion can comprise the bulk of most problem-solving activities and by the end of the process, you might find that your team is talked out! 

Providing a means for your team to give feedback with short written notes can ensure everyone is head and can contribute without the need to stand up and talk. Depending on the needs of the group, giving an alternative can help ensure everyone can contribute to your problem-solving model in the way that makes the most sense for them.

Response Cards is a great way to close a workshop if you are looking for a gentle warm-down and want to get some swift discussion around some of the feedback that is raised. 

Response Cards   #debriefing   #closing   #structured sharing   #questions and answers   #thiagi   #action   It can be hard to involve everyone during a closing of a session. Some might stay in the background or get unheard because of louder participants. However, with the use of Response Cards, everyone will be involved in providing feedback or clarify questions at the end of a session.

Save time and effort discovering the right solutions

A structured problem solving process is a surefire way of solving tough problems, discovering creative solutions and driving organizational change. But how can you design for successful outcomes?

With SessionLab, it’s easy to design engaging workshops that deliver results. Drag, drop and reorder blocks  to build your agenda. When you make changes or update your agenda, your session  timing   adjusts automatically , saving you time on manual adjustments.

Collaborating with stakeholders or clients? Share your agenda with a single click and collaborate in real-time. No more sending documents back and forth over email.

Explore  how to use SessionLab  to design effective problem solving workshops or  watch this five minute video  to see the planner in action!

Over to you

The problem-solving process can often be as complicated and multifaceted as the problems they are set-up to solve. With the right problem-solving techniques and a mix of creative exercises designed to guide discussion and generate purposeful ideas, we hope we’ve given you the tools to find the best solutions as simply and easily as possible.

Is there a problem-solving technique that you are missing here? Do you have a favorite activity or method you use when facilitating? Let us know in the comments below, we’d love to hear from you! 

thank you very much for these excellent techniques

Certainly wonderful article, very detailed. Shared!

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• Prof. Samuel Bowring

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• Earth, Atmospheric, and Planetary Sciences

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• Environmental Management
• Atmospheric Science
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Solving complex problems, course description.

12.000 Solving Complex Problems is designed to provide students the opportunity to work as part of a team to propose solutions to a complex problem that requires an interdisciplinary approach. For the students of the class of 2013, 12.000 will revolve around the issues associated with what we can and must do about the …

12.000 Solving Complex Problems is designed to provide students the opportunity to work as part of a team to propose solutions to a complex problem that requires an interdisciplinary approach. For the students of the class of 2013, 12.000 will revolve around the issues associated with what we can and must do about the steadily increasing amounts CO 2 in Earth’s atmosphere.

12.000 is a core course for the MIT Terrascope freshman learning community. Each year’s class explores a different problem in detail through the study of complementary case histories and the development of creative solution strategies. It includes training in Web site development, effective written and oral communication, and team building. Initially developed with major financial support from the d’Arbeloff Fund for Excellence in Education , 12.000 is designed to enhance the freshman experience by helping students develop contexts for other subjects in the sciences and humanities, and by helping them to establish learning communities that include upperclassmen, faculty, MIT alumni, and professionals in science and engineering fields.

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A Crash Course in Critical Thinking

What you need to know—and read—about one of the essential skills needed today..

Posted April 8, 2024 | Reviewed by Michelle Quirk

• In research for "A More Beautiful Question," I did a deep dive into the current crisis in critical thinking.
• Many people may think of themselves as critical thinkers, but they actually are not.
• Here is a series of questions you can ask yourself to try to ensure that you are thinking critically.

Conspiracy theories. Inability to distinguish facts from falsehoods. Widespread confusion about who and what to believe.

These are some of the hallmarks of the current crisis in critical thinking—which just might be the issue of our times. Because if people aren’t willing or able to think critically as they choose potential leaders, they’re apt to choose bad ones. And if they can’t judge whether the information they’re receiving is sound, they may follow faulty advice while ignoring recommendations that are science-based and solid (and perhaps life-saving).

Moreover, as a society, if we can’t think critically about the many serious challenges we face, it becomes more difficult to agree on what those challenges are—much less solve them.

On a personal level, critical thinking can enable you to make better everyday decisions. It can help you make sense of an increasingly complex and confusing world.

In the new expanded edition of my book A More Beautiful Question ( AMBQ ), I took a deep dive into critical thinking. Here are a few key things I learned.

First off, before you can get better at critical thinking, you should understand what it is. It’s not just about being a skeptic. When thinking critically, we are thoughtfully reasoning, evaluating, and making decisions based on evidence and logic. And—perhaps most important—while doing this, a critical thinker always strives to be open-minded and fair-minded . That’s not easy: It demands that you constantly question your assumptions and biases and that you always remain open to considering opposing views.

In today’s polarized environment, many people think of themselves as critical thinkers simply because they ask skeptical questions—often directed at, say, certain government policies or ideas espoused by those on the “other side” of the political divide. The problem is, they may not be asking these questions with an open mind or a willingness to fairly consider opposing views.

When people do this, they’re engaging in “weak-sense critical thinking”—a term popularized by the late Richard Paul, a co-founder of The Foundation for Critical Thinking . “Weak-sense critical thinking” means applying the tools and practices of critical thinking—questioning, investigating, evaluating—but with the sole purpose of confirming one’s own bias or serving an agenda.

In AMBQ , I lay out a series of questions you can ask yourself to try to ensure that you’re thinking critically. Here are some of the questions to consider:

• Why do I believe what I believe?
• Are my views based on evidence?
• Have I fairly and thoughtfully considered differing viewpoints?
• Am I truly open to changing my mind?

Of course, becoming a better critical thinker is not as simple as just asking yourself a few questions. Critical thinking is a habit of mind that must be developed and strengthened over time. In effect, you must train yourself to think in a manner that is more effortful, aware, grounded, and balanced.

For those interested in giving themselves a crash course in critical thinking—something I did myself, as I was working on my book—I thought it might be helpful to share a list of some of the books that have shaped my own thinking on this subject. As a self-interested author, I naturally would suggest that you start with the new 10th-anniversary edition of A More Beautiful Question , but beyond that, here are the top eight critical-thinking books I’d recommend.

The Demon-Haunted World: Science as a Candle in the Dark , by Carl Sagan

This book simply must top the list, because the late scientist and author Carl Sagan continues to be such a bright shining light in the critical thinking universe. Chapter 12 includes the details on Sagan’s famous “baloney detection kit,” a collection of lessons and tips on how to deal with bogus arguments and logical fallacies.

Clear Thinking: Turning Ordinary Moments Into Extraordinary Results , by Shane Parrish

The creator of the Farnham Street website and host of the “Knowledge Project” podcast explains how to contend with biases and unconscious reactions so you can make better everyday decisions. It contains insights from many of the brilliant thinkers Shane has studied.

Good Thinking: Why Flawed Logic Puts Us All at Risk and How Critical Thinking Can Save the World , by David Robert Grimes

A brilliant, comprehensive 2021 book on critical thinking that, to my mind, hasn’t received nearly enough attention . The scientist Grimes dissects bad thinking, shows why it persists, and offers the tools to defeat it.

Think Again: The Power of Knowing What You Don't Know , by Adam Grant

Intellectual humility—being willing to admit that you might be wrong—is what this book is primarily about. But Adam, the renowned Wharton psychology professor and bestselling author, takes the reader on a mind-opening journey with colorful stories and characters.

Think Like a Detective: A Kid's Guide to Critical Thinking , by David Pakman

The popular YouTuber and podcast host Pakman—normally known for talking politics —has written a terrific primer on critical thinking for children. The illustrated book presents critical thinking as a “superpower” that enables kids to unlock mysteries and dig for truth. (I also recommend Pakman’s second kids’ book called Think Like a Scientist .)

Rationality: What It Is, Why It Seems Scarce, Why It Matters , by Steven Pinker

The Harvard psychology professor Pinker tackles conspiracy theories head-on but also explores concepts involving risk/reward, probability and randomness, and correlation/causation. And if that strikes you as daunting, be assured that Pinker makes it lively and accessible.

How Minds Change: The Surprising Science of Belief, Opinion and Persuasion , by David McRaney

David is a science writer who hosts the popular podcast “You Are Not So Smart” (and his ideas are featured in A More Beautiful Question ). His well-written book looks at ways you can actually get through to people who see the world very differently than you (hint: bludgeoning them with facts definitely won’t work).

A Healthy Democracy's Best Hope: Building the Critical Thinking Habit , by M Neil Browne and Chelsea Kulhanek

Neil Browne, author of the seminal Asking the Right Questions: A Guide to Critical Thinking, has been a pioneer in presenting critical thinking as a question-based approach to making sense of the world around us. His newest book, co-authored with Chelsea Kulhanek, breaks down critical thinking into “11 explosive questions”—including the “priors question” (which challenges us to question assumptions), the “evidence question” (focusing on how to evaluate and weigh evidence), and the “humility question” (which reminds us that a critical thinker must be humble enough to consider the possibility of being wrong).

Warren Berger is a longtime journalist and author of A More Beautiful Question .

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5 courses from MIT ranked among the most popular of 2024 — and all time

Five courses from mit ranked among the most popular of 2024 — and all time, class central recognized mitx’s free online courses among the 250 most popular of all time and 100 most popular of 2024..

By Katherine Ouellette

Four MITx courses are ranked among Class Central’s list of 250 Most Popular Online Courses of All Time for 2024. Another MITx course is recognized as one of the 100 Most Popular Free Online Courses of 2024 . Class Central, a resource for curating and rating online courses, compiled these lists based on the number of enrollments reported across more than 200,000 online courses aggregated in its catalog.

Discover why these free and low-cost online courses on aerodynamics, computer programming, and supply chains are so popular with tens of thousands of learners. Start learning from MIT faculty today!

Introduction to Aerodynamics

Discover the basic fluid dynamic concepts behind aircraft analysis and design.

Introduction to Computer Science and Programming Using Python

An introduction to computer science as a tool to solve real-world analytical problems using Python 3.5.

Machine Learning with Python: from Linear Models to Deep Learning

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. Part of the MITx MicroMasters Program in Statistics and Data Science , developed jointly with the MIT Institute for Data, Systems, and Society .

Supply Chain Analytics

Master and apply the core methodologies used in supply chain analysis and modeling, including statistics, regression, optimization, and probability. Part of the MITx MicroMasters Program in Supply Chain Management , developed jointly with the MIT Center for Transportation & Logistics .

Supply Chain Fundamentals

Learn fundamental concepts for logistics and supply chain management from both analytical and practical perspectives. Part of the MITx MicroMasters Program in Supply Chain Management , developed jointly with the MIT Center for Transportation & Logistics .

5 courses from MIT ranked among the most popular of 2024 — and all time was originally published in MIT Open Learning on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Original research article, lightweight underwater image adaptive enhancement based on zero-reference parameter estimation network.

• 1 School of Mechanical and Power Engineering, Dalian Ocean University, Dalian, China
• 2 School of Information Engineering, Dalian Ocean University, Dalian, China

Underwater images suffer from severe color attenuation and contrast reduction due to the poor and complex lighting conditions in the water. Most mainstream methods employing deep learning typically require extensive underwater paired training data, resulting in complex network structures, long training time, and high computational cost. To address this issue, a novel ZeroReference Parameter Estimation Network (Zero-UAE) model is proposed in this paper for the adaptive enhancement of underwater images. Based on the principle of light attenuation curves, an underwater adaptive curve model is designed to eliminate uneven underwater illumination and color bias. A lightweight parameter estimation network is designed to estimate dynamic parameters of underwater adaptive curve models. A tailored set of non-reference loss functions are developed for underwater scenarios to fine-tune underwater images, enhancing the network’s generalization capabilities. These functions implicitly control the learning preferences of the network and effectively solve the problems of color bias and uneven illumination in underwater images without additional datasets. The proposed method examined on three widely used real-world underwater image enhancement datasets. Experimental results demonstrate that our method performs adaptive enhancement on underwater images. Meanwhile, the proposed method yields competitive performance compared with state-of-the-art other methods. Moreover, the Zero-UAE model requires only 17K parameters, minimizing the hardware requirements for underwater detection tasks. What’more, the adaptive enhancement capability of the Zero-UAE model offers a new solution for processing images under extreme underwater conditions, thus contributing to the advancement of underwater autonomous monitoring and ocean exploration technologies.

1 Introduction

Since the particulate matter in the water leads to light absorption and scattering, the underwater observation tasks based on optical vision face enormous challenges. Underwater images inevitably suffer from quality degradation issues caused by wavelength and distance-dependent attenuation and scattering ( Akkaynak et al., 2017 ). Typically, when the light propagates through water, it suffers from selective attenuation that results in various degrees of color deviations. In water, red light with a longer wavelength is absorbed more than green and blue light, so it attenuates fastest. Conversely, light with the blue-green wavelength experiences the slowest attenuation, resulting in most underwater images appearing in bluegreen tones ( Kocak et al., 2008 ). In this environment, it is critical to identify effective solutions for improving the visual quality of underwater images and for a better understanding the underwater world.

Given the challenges faced by underwater optical imaging, Synthetic Aperture Sonar(SAS) imaging technology based on sound waves may offer some solutions ( Zhang et al., 2021 ; Yang, 2023 ). Unlike optical imaging, SAS utilizes the propagation characteristics of sound waves in water to penetrate through particles and acquire high-resolution underwater images. Sound waves propagate in water without being affected by light absorption and scattering, thus overcoming the quality degradation issues encountered in optical imaging. However, the resolution of SAS imaging is typically influenced by underwater propagation media such as water temperature, salinity, and water flow velocity. SAS imaging often requires complex signal processing, data processing techniques, and corresponding hardware equipment, potentially increasing system costs and complexity ( Abu and Diamant, 2023 ). Therefore, despite the significant advantages of SAS imaging technology in underwater observation, the focus of this study remains on the processing and analysis of underwater optical images. This aims to explore effective methods for improving the visual quality of underwater images, thereby enhancing our understanding of the underwater environment.

Furthermore, when conducting underwater observation tasks, the selection of lightweight equipment is crucial to enhance maneuverability, flexibility, reducing complexity, and cutting costs. Despite the potential for slight performance degradation associated with lightweight devices, this is a factor that needs to be balanced when effectively executing tasks. In this context, the adoption of lightweight methods for processing underwater images becomes particularly important, as they can enhance in real-time the visual quality of underwater images, contributing to a more accurate understanding of the underwater environment.

In order to obtain higher visual quality underwater images, methods based on physical models can, to some extent, address the aforementioned issues ( Zhuang, 2021 ). In the field of underwater image enhancement, physics-based methods ( Chiang and Chen, 2011 ; Drews et al., 2016 ; Li et al., 2016 ; Berman et al., 2017 ; Zhuang et al., 2021 ) focus on accurately estimating medium transmission. By utilizing estimated parameters such as medium transmittance, uniform background light, and other critical underwater imaging parameters, these methods invert the physical model of underwater imaging to obtain clear images. Although these methods perform well in certain scenarios, they often produce unstable and sensitive results when dealing with challenging underwater environments. These methods include histogram equalization (HE) ( Frei, 1977 ) and contrast-limited adaptive histogram equalization (CLAHE) ( Zuiderveld, 1994 ), aim to adjust pixel values to enhance specific qualities of the image, such as color, contrast, and brightness. Image restoration methods (UDCP) ( Drews et al., 2016 ) view improving image quality as the inverse imaging problem. Though methods based on physical models can exhibit satisfactory performance in certain scenarios, they typically generate unstable and sensitive results when facing challenging underwater scenarios. There are two reasons for this: 1) estimating multiple underwater imaging parameters is intricate for traditional methods, and 2) the assumed underwater imaging models do not work well.

In recent years, significant progress ( Cai et al., 2018 ; Li et al., 2020b ) has been made in underwater image enhancement using deep learning technologies. ( Wang et al., 2021 ; Huang et al., 2022 ; Lai et al., 2022 ) showed that convolutional neural network (CNN) based image enhancement algorithms perform well on underwater images, achieving enhanced images with improved contrast and color reproduction. The method in ( Xiao et al., 2022 ) introduced a CNN-based image enhancement framework for underwater images that is able to automatically determine optimal parameters for enhancing underwater images, resulting in images with both high quality and low computational cost. This method has achieved state-of-the-art performance compared to prior work in image enhancement for underwater images. However, most of these methods rely on paired data for supervised training, and even though some unsupervised learning methods do not require paired data, they still necessitate unpaired reference data. Unfortunately, collecting paired data introduces high costs, and images generated by simulation algorithms differ from real data, leading to lower generalization capabilities of the network. Different from these papers, the proposed deep learning-based methods possess a unique advantage—zero-reference. Throughout the training process, it does not require any paired or unpaired data, in stark contrast to existing CNN and GAN-based methods that rely on such data.

Inspired by Zero-DCE ( Guo et al., 2020 ), this paper specifically designs an underwater curve model that applies the concept of zero-reference learning to underwater scenarios. A new deep learning method called Zero-UAE is proposed, which is based on a zero-reference parameter estimation network, for adaptive enhancement of underwater images. This method does not use an end-to-end network model because such a model is much more complex than parameter estimation. Only relying on a small amount of non-reference data samples, the training effectiveness of an end-to-end network model always cannot achieve expectations. In order to achieve lightweight and zero-reference better while ensuring the robustness of the network, an adaptive recovery image parameter estimation network is needed, which as simple as possible. Unlike the training method proposed in Zero-DCE, due to the complexity of the underwater environment, which cannot use multi-sequence datasets for guidance, this method only uses a limited number of underwater image datasets for guidance. Zero-UAE can adaptively enhance the brightness and contrast of images while restoring normal colors and details to underwater images. This method demonstrates that even in zero-reference training scenarios, Zero-UAE remains competitive in comparison with state-of-the-art methods that require paired or unpaired data. The contributions of this method can be summarized as follows:

1. A zero-reference underwater adaptive enhancement parameter estimation network is proposed, which does not rely on paired or unpaired data, thereby reducing the risk of overfitting. This study demonstrates robustness in various complex underwater conditions.

2. A set of non-reference loss functions is designed, including the specifically crafted underwater color adaptive correction loss function proposed in this paper. Through their collaborative action, these loss functions effectively facilitate the adaptive enhancement of degraded images in complex underwater scenes while ensuring pixel consistency.

3. Zero-UAE achieves state-of-the-art performance on several recent benchmarks, both in terms of visual quality and quantitative metrics.

Furthermore, the Zero-UAE method performs excellently in underwater survey tasks, including various marine life, seabed debris, corals, sand, without incurring significant computational burdens. With a small model size, real-time image processing can be achieved in just 30 minutes of training time. This offers a more convenient option for devices in underwater observation tasks.

The rest of this paper is organized as follows. Section II presents the related works of underwater image enhancement. Section III introduces the proposed method. In Section IV, the qualitative and quantitative experiments are conducted. Section V concludes this paper.

2 Related works

Underwater image enhancement is generally categorized into two major groups: traditional methods and deep learning methods. Traditional methods are further divided into non-physical model-based methods and physical model-based methods.

2.1 Traditional methods

Non-physical model-based methods focus on directly intensifying pixel values to achieve improved image quality without the constraints of physical models. ( Ancuti et al., 2012 ) proposed a fusion-based method that applies a multiscale fusion strategy on images subjected to color correction and contrast enhancement. In ( Ancuti et al., 2017 ; Ghani and Isa, 2015 ) introduced a contrast enhancement method that aligns with the Rayleigh distribution in RGB color space. Another technical method utilizes the Retinex theorem for algorithm design, where ( Fu et al., 2014 ) converts color-corrected images into the CIELab color space and enhances the L channel using the Retinex theorem. Methods based on physical models treat underwater image enhancement as an inverse problem, introducing various priors and models of underwater image formation. Among these, the notable model is the Jaffe-McGlamery underwater image model ( McGlamery, 1980 ; Jaffe, 1990 ).

2.2 Deep learning models

In recent years, deep learning methods have been widely applied in the field of underwater image processing, primarily focusing on acquiring training datasets and the generalization capability of convolutional models. These methods mainly include methods based on Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN).

( Li et al., 2020a ) introduced the Underwater Image Enhancement Convolutional Neural Network (UWCNN), reconstructing clear underwater images directly using underwater scene priors without estimating model parameters. ( Qi et al., 2022 ) proposed a novel underwater image enhancement network (SGUIE-Net), which addresses the issues of color distortion and detail blurring in underwater images by incorporating semantic information and region-wise enhancement feature learning. ( Wang et al., 2021 ) proposed an underwater image enhancement convolutional neural network ( UICE 2 -Net) that utilizes two color spaces. This method is the first one based on deep learning to use the HSV color space for underwater image enhancement.

( Guo et al., 2019 ) proposed a Multiscale Dense Generative Adversarial Network (GAN) for underwater image enhancement, employing multiscale dense residual blocks in the generator to improve performance and retain finer details. They used spectral normalization to stabilize discriminator training and designed a non-saturating GAN loss function to constrain the training. ( Cao et al., 2018 ) utilized two neural networks to estimate background light and scene depth separately to restore underwater images, improving the color information of underwater images ( Fabbri et al., 2018 ), by improving the loss function of the Generative Adversarial Network, trained a paired underwater image dataset generated using CycleGAN to obtain enhanced images with better color effects. ( Li et al., 2017 ) proposed an Unsupervised Generative Adversarial Network (WaterGAN), taking aerial images and depth pairs as input to generate synthesized underwater images. Subsequently, they introduced a color correction network, taking original unlabeled underwater images as input and outputting restored underwater images. ( Wang et al., 2019 ) introduced an Unsupervised Generative Adversarial Network (UWGAN) based on an improved underwater imaging model for generating lifelike underwater images from aerial images and depth maps. They further utilized U-Net for color restoration and dehazing training on a synthetic underwater dataset. ( Islam et al., 2020b ) introduced a method for fast underwater image enhancement to enhance visual perception (FUnIEGAN). They proposed a model based on conditional generative adversarial networks for real-time underwater image enhancement. Moreover, they contributed to the EUVP dataset, which includes a collection of paired and unpaired underwater images. ( Wang et al., 2023 ) proposed a generative adversarial network with multi-scale and attention mechanisms, which introduces multi-scale dilated convolution and directs the network’s focus towards important features, thus reducing the interference from redundant feature information.

( Huang et al., 2023 ) introduced a Zero-Reference Deep Network that is designed based on the classical haze image formation principle, aiming to explore zero-reference learning for underwater image enhancement. ( Xie et al., 2023 ) proposed a zero-shot dehazing network that further improved the level adjustment method combined with automatic contrast for enhancement.

Currently, many deep learning-based underwater image enhancement methods employ a supervised learning method that relies on paired training data generated by simulation methods. However, this method faces several challenges. Firstly, supervised learning requires a substantial amount of paired data, and in the deep-sea environment, the difficulty and cost of obtaining real paired data make this method impractical. Secondly, due to the complexity of the deep-sea environment, simulated image pairs may not fully capture the diversity and details of the actual scenes, thereby affecting the network’s generalization ability. In comparison to supervised learning, there are some unsupervised learning methods that do not require paired data, but they still necessitate non-paired data for training. Despite the efforts of zero-shot underwater image enhancement to improve the quality of underwater images, the deep-sea environment presents unique challenges. Factors such as lighting conditions, water quality variations, and the diversity of underwater objects make training models challenging.

Therefore, this paper proposes a novel lightweight underwater image adaptive enhancement method based on Zero-UAE. In contrast to existing deep learning-based underwater image enhancement methods, this paper has the following unique characteristics: 1) It adopts a zero-reference learning strategy, eliminating the need for paired and unpaired data. 2) It designs an underwater adaptive curve model based on the principle of light attenuation curves to eliminate uneven underwater illumination and color distortion. 3) The paper employs a non-end-to-end network structure, acquiring low-level features through skip connections, capable of handling most underwater scenes. 4) It devises a set of underwater image non-reference loss functions, reinforcing the pixel structure of underwater images and enhancing their visual effects compared to other methods.

3 Methodology

Typically, collecting enough paired data in underwater scenes incurs high costs, and simulated underwater images differ from real ones. Consequently, supervised underwater image enhancement methods relying on paired datasets are limited due to their relatively poor generalization ability, additional artifacts, and color shifts. Although unsupervised underwater image enhancement doesn’t require paired datasets, it still necessitates carefully selected unpaired training data. Recognizing the challenges of insufficient image samples and acquiring paired/unpaired images in certain underwater scenarios, this paper proposes an underwater image adaptive enhancement framework based on Zero-UAE. Compared to other deep learning methods, the training process of the proposed method doesn’t rely on any reference images. Additionally, to adapt to the unique characteristics of the deep-sea environment, this study specifically devises a lightweight network architecture and employs non-reference loss functions tailored for underwater scenes to enhance the network’s generalization capabilities. The objective of this method is to make deep learning more practical and effective in the field of underwater image processing.

The proposed Zero-UAE framework, as shown in Figure 1 , relies solely on pixel features from a limited number of non-reference underwater data samples. Image enhancement is achieved through a straightforward mapping of underwater adaptive enhancement curves. This framework comprises a crucial component known as UAE-Net (Underwater Adaptive Enhancement Parameter Estimation Network), tasked with estimating the optimal fit of the underwater adaptive enhancement curve (UAE curve) for a given input image. Subsequently, the framework iteratively applies these curves, systematically mapping all pixels within the input RGB channels, ultimately generating the enhanced image. The key components of Zero-UAE will be detailed in subsequent sections, including UAE curves, UAE-Net, and non-reference loss functions.

Figure 1 Framework of Zero-UAE.

3.1 Underwater adaptive enhancement curve

Inspired by the curve adjustment feature in photo editing software and the Zero-DCE method proposed by ( Guo et al., 2020 ), this study presents a curve model suitable for underwater adaptive image enhancement. We utilize the curve adjustment method to automatically map degraded underwater images to normal underwater images, where the network-estimated parameter feature mapping relies entirely on the input image. When designing a differentiable curve model for underwater parameter mapping, there are two requirements: 1) Each pixel value of the enhanced underwater image should be within the normalized range of [0,1] to avoid information loss, which can lead to severe color bias; 2) The curve should be monotonous to maintain the differences between neighboring pixels. To achieve the requirements, a design similar to the previously mentioned quadratic curve was adopted, which can be represented as:

where x represents pixel coordinates, and UAE ( I ( x ); β ) denotes the enhanced version of the given input I ( x ). β ∈ [−1,1] is a trainable curve parameter, learned through the underwater adaptive enhancement parameter estimation network, used to adjust the magnitude of the UAE curve and control the level of underwater image enhancement. In order to preserve color information in underwater images better, the curve is separately applied to the three RGB channels of the image. Specifically, the UAE curve defined in Equation (1) can be iteratively applied for more versatile adjustments, adapting to complex underwater conditions. This can be expressed by the following formula:

where n is the iteration number controlling the curvature. The value of n is set to 8, which can deal with most cases satisfactorily. This method takes into account more flexibility in adapting to color variations and brightness differences in underwater images. Because β is applied to all pixels, global adjustments may lead to potential local over-enhancement/under-enhancement issues in underwater images. To further enhance the capability of processing underwater images, this study formulates δ as a pixel-wise parameter, i.e., each pixel of the given input image has a corresponding curve with the best-fitting δ to adjust its dynamic range, referred to as the underwater color adaptive recovery map and denoted as δ , it has been introduced. Consequently, Equation (2) can be expressed as Equation (3) :

where δ is a parameter map of the same size as the given image. Here, this paper assumes that pixels in a local region have the same intensity (also the same adjustment curves), and thus the neighboring pixels in the output result still preserve the monotonous relations. This pixel-wise higher-order curve not only adapts to underwater conditions better but also ensures the goals of normalization, monotonicity, and simplicity.

An example of the pixel-wise curve parameter maps is shown in Figure 2 . The curve parameter maps for the three channels of the input image and the resulting image were respectively illustrated, showcasing the adaptability of this new feature to underwater images. This included the best-fitting parameter maps that accurately reflected changes in different regions. The effectiveness of revealing details in each region of the underwater image was demonstrated through pixel-wise curve mapping.

Figure 2 An example of the pixel-wise curve parameter maps.

3.2 UAE-Net

To understand the relationship between input images and their most suitable underwater adaptive enhancement curves, this paper transforms the underwater image enhancement task into an estimation problem of specific curve parameters, rather than directly conducting end-to-end mapping. End-to-end models are much more complex than parameter mapping estimation. For complex end-to-end networks, training results often fall short of expectations when relying on only a small number of samples without reference data. To better achieve lightweight and zero-reference characteristics, for parameter mapping estimation tasks, the network needs to be designed as simple as possible. Therefore, this paper designs an Underwater Adaptive Enhancement Parameter Estimation Network (UAE-Net), as shown in Figure 3 . This network takes underwater images as input and outputs a series of pixel-level curve parameter maps corresponding to higher-order curves. The network consists of three layers of traditional convolution and four layers of depth-wise separable convolution. The first two layers contain 32 convolutional kernels of size 3×3 with a stride of 1, using the LeakyReLU activation function; the third layer comprises 32 convolutional kernels of size 1×1 with a stride of 1, also using LeakyReLU. To capture advanced color features of a large number of underwater degraded images while maintaining the relationship between neighboring pixels, both the fourth and fifth layers of depth-wise separable convolution take parameters from the third layer and incorporate a GroupNorm layer. Some skip connections are used to introduce the features from shallow convolutional layers to obtain rich low-level information. The final convolutional layer is followed by the Tanh activation function, generating parameter maps distributed over 8 iterations (n=8), where each iteration produces three curve parameter maps for each of the three channels. It is noteworthy that UAE-Net has only 17,699 trainable parameters and 1.15 billion floating-point operations (FLOPs), making it suitable for processing input images of size 256×256×3. More detailed network resource information is provided in Table 1 . Therefore, this network is extremely lightweight so it is suitable for deployment on computationally limited devices, such as underwater exploration robots.

Figure 3 Network structure of UAE-Net.

Table 1 Resource occupancy of UAE-Net.

3.3 Nonreference loss functions

To achieve zero-reference learning in UAE-Net, a set of differentiable non-reference loss functions was designed to train the network, aimed at adapting to the unique characteristics of underwater images for effective network training. This series of loss functions not only serves training purposes but also implicitly evaluates the quality of image enhancement. An underwater color adaptive recovery loss is employed to restore image colors, correcting potential color biases in the enhanced image and establishing relationships among the three adjustment channels. Additionally, an illumination smoothness loss is introduced to maintain a monotonic relationship between adjacent pixels, coupled with exposure control loss for effective exposure level management. Such a multi-loss strategy aids in comprehensively considering various aspects of image quality, enhancing the network’s performance in underwater environments.

3.3.1 Underwater color adaptive correction loss

Drawing inspiration from the underwater image fusion algorithm ( Babu et al., 2023 ), which utilizes the concept of combining histogram stretching, contrast enhancement, and color balancing, we design an underwater color adaptive correction loss L uac that can be expressed as Equation (4) :

in this context, τ represents the enhanced image, while R,G and B correspond to the values of the three channels in the enhanced image, respectively. The smaller the underwater color Adaptive correction loss, the closer the average values of the RGB components are to each other, and the closer the output image is to the real world.

3.3.2 Exposure control loss

To control exposure levels and mitigate underexposed or overexposed areas, this study employed an exposure control loss, L exp , which measures the distance between the average intensity of local areas and a well-exposed reference level E . Following existing practices ( Mertens et al., 2009 , 2007 ), E was set as the gray level in the RGB color space. E was adjusted to 0.43. M determines the patch size for processing images, and based on experimental results and performance evaluations, this paper sets M to 32. The loss L exp can be expressed as Equation (5) :

where M represents the number of nonoverlapping local regions of size 32×32, Y is the average intensity value of a local region in the enhanced image.

3.3.3 Illumination smoothness loss

To maintain the monotonic relationships between adjacent pixels, an illumination smoothness loss is incorporated into each curve parameter map δ . The illumination smoothness loss L TVδ can be expressed as Equation (6) :

where N is the number of iteration, ∇ x and ∇ y represent the horizontal and vertical gradient operations, respectively.

3.3.4 Spatial consistency loss

The spatial consistency loss L spa encourages spatial coherence of the enhanced image through preserving the difference of neighboring regions between the input image and its enhanced version. The spatial consistency loss L spa can be expressed as Equation (7) :

where K is the number of local regions, and Ω( i ) represents the four neighboring regions (top, down, left, right) centered at the region i . This study denotes Y and I as the average intensity values of the local region in the enhanced version and input image, respectively. The size of the local region is empirically set to 4×4. This loss is stable given other region sizes.

3.3.5 Total loss

The total loss can be expressed as Equation (8) :

where W u w c o l o r W e x p W s p a and W t v δ are the weights of the losses.

4 Experiments

In order to enhance the network’s generalization performance, underwater images of various degradation types are incorporated into the training set. Specifically, 1000 images from the SUIM dataset ( Islam et al., 2020a ) and 800 underwater images from the NUICNet dataset ( Cao et al., 2020 ) are selected for training. The number of iterations is set to 100. The experiment is implemented using the PyTorch framework, and the training images are resized to 256 × 256 × 3. The Adam optimizer is used with default parameters and a fixed learning rate of 1e-4. The experimental environment includes an NVIDIA GeForce RTX 3080Ti GPU, 32GB RAM, and an AMD Ryzen 7-5800X CPU.

Several underwater image processing algorithms were compared, including two traditional methods, three supervised methods, and one similar unsupervised method: the underwater depth estimation and image restoration method (UDCP) by ( Drews et al., 2016 ), the underwater image restoration method based on image blurring and light absorption (IBLA) by ( Peng and Cosman, 2017 ), the underwater image enhancement network (UWCNN) by ( Li et al., 2020a ), fast underwater image enhancement to enhance visual perception (FUnIEGAN) by ( Islam et al., 2020b ), the medium transmission guided multi-color space embedding (Ucolor) underwater image enhancement method by ( Li et al., 2021 ), and the unsupervised underwater image restoration method (UDNet) by ( Saleh et al., 2022 ).

4.1 Evaluation on RUIE data sets

To evaluate the effectiveness of the proposed method across different standards, this paper selected 100 underwater photographs from the RUIE dataset ( Liu et al., 2020 ). Several non-reference image quality assessment metrics were employed, including Underwater Image Quality Metric (UIQM) ( Panetta et al., 2015 ), Multi-Scale Image Quality Transformer (MUSIQ) ( Ke et al., 2021 ), and No-Reference Image Quality Evaluator (NIQE) ( Mittal et al., 2012 ). Higher UIQM and MUSIQ values indicate better algorithm performance, while lower NIQE values signify better performance. In comparative experiments, the proposed method demonstrated the best performance with UIQM and NIQE evaluation metrics scoring 5.2196 and 3.3951, respectively, and maintained competitiveness in the MUSIQ evaluation metric as well.

4.1.1 Quantitative performance analysis

Table 2 presents the average quantitative evaluation of the RUIE dataset. Among the results, red indicates the best performance, and green signifies the second best. Moreover, an upward arrow denotes that higher values represent better algorithm performance, while a downward arrow signifies that lower values indicate better performance. It can be observed that Ucolor and UDnet exhibit suboptimal performance on UIQM and NIQE, respectively. In contrast, the proposed method achieves optimal levels across the entire dataset. It is noteworthy that, unlike other deep learning methods, the proposed method does not utilize any reference images during the training process. Overall, extensive experiments on benchmark datasets demonstrate that the proposed method outperforms current state-of-the-art methods both subjectively and objectively, showcasing the potential of zero-reference image enhancement in underwater applications.

Table 2 Quantitative evaluation on RUIE Datasets using UIQM, NIQE, and MUSIQ metrics.

4.1.2 Performance evaluation

As shown in Figure 4 , UDCP performs poorly in enhancing image brightness and color. While the IBLA method exhibits issues of blurring and color bias, it is not entirely effective. The UWCNN and FUnIE-GAN exhibit suboptimal performance on the dataset. Despite making some progress in color adjustment, they cannot completely solve the problem of color distortion. Additionally, in terms of brightness enhancement, they demonstrate certain shortcomings in their ability to improve the overall brightness of images. Despite its ability to increase brightness, Ucolor is unable to fully rectify color distortion issues. The unsupervised scheme UDNet also fails to completely eliminate color bias. In contrast, our proposed method demonstrates outstanding performance in color restoration, contrast enhancement, and brightness improvement.

Figure 4 Comparison on RUIE data sets. (A) Original image. (B) UDCP ( Drews et al., 2016 ). (C) IBLA ( Peng and Cosman, 2017 ). (D) UWCNN ( Li et al., 2020a ). (E) FUnIEGAN ( Islam et al., 2020b ). (F) Ucolor ( Li et al., 2021 ). (G) UDNet ( Saleh et al., 2022 ). (H) Proposed.

4.2 Evaluation on UIEB data sets

To comprehensively assess the quantitative performance of the proposed method on the UIEB dataset ( Li et al., 2019 ), 800 underwater images are selected for evaluation. Performance evaluation uses the nonreference metrics UIQM, NIQE, and MUSIQ. In comparative experiments, the proposed method performs the best in the NIQE and MUSIQ evaluation metrics, scoring 4.4538 and 49.8793, respectively.

4.2.1 Quantitative performance analysis

Table 3 presents the average evaluation results of UIQM, NIQE, and MUSIQ metrics on the UIEB dataset. The proposed algorithm achieved either optimal or suboptimal results on most images. While the similar unsupervised scheme UDNet showed acceptable results on some images, the proposed method consistently obtained optimal values across the entire dataset.

Table 3 Quantitative evaluation on UIEB Datasets Using UIQM, NIQE, and MUSIQ metrics.

4.2.2 Performance evaluation

As observed in Figure 5 , various existing methods exhibited different shortcomings. UDCP failed to effectively eliminate color cast, while IBLA introduced brightness distortion in certain images. Although UWCNN, Ucolor, and UDNet showed some capability in removing haze and blur, issues with color cast persisted in some images, and UWCNN suffered from insufficient brightness. FUnIE-GAN managed to restore color in most images but encountered difficulties with specific ones, resulting in a grayish tone. In contrast, the proposed method outperformed in color restoration and contrast enhancement, particularly excelling in target restoration and brightness improvement.

Figure 5 Comparison on UIEB data sets. (A) Original image. (B) UDCP ( Drews et al., 2016 ). (C) IBLA ( Peng and Cosman, 2017 ). (D) UWCNN ( Li et al., 2020a ). (E) FUnIEGAN ( Islam et al., 2020b ). (F) Ucolor ( Li et al., 2021 ). (G) UDNet ( Saleh et al., 2022 ). (H) Proposed.

4.3 Evaluation on U45 data sets

To validate the performance of the proposed method across multiple benchmark tests, this paper performs experiments on the U45 dataset and assesses its performance using non-reference metrics such as UIQM, MUSIQ, and NIQE. In comparative experiments, the proposed method performs the best in the NIQE and MUSIQ evaluation metrics, scoring 4.4738 and 47.1163, respectively.

4.3.1 Quantitative performance analysis

Table 4 presents the average quantitative evaluation of the U45 dataset. The proposed method achieves the optimal level on the dataset at both NIQE and MUSIQ metrics. In contrast, traditional algorithms UDCP and Ucolor show suboptimal performance.

Table 4 Quantitative evaluation on U45 Datasets using UIQM, NIQE, and MUSIQ metrics.

4.3.2 Performance evaluation

As shown in Figure 6 , UDCP performs poorly in enhancing image brightness and color. Although the IBLA method exhibits issues of blurring and color bias, it is not entirely ineffective. UWCNN and FUnIE-GAN both exhibit problems such as excessive saturation and uneven brightness in the U45 dataset. Despite some adjustments in saturation, Ucolor and UDNet are unable to fully correct color distortion issues in underwater images. In contrast, our proposed method demonstrates good performance in color restoration, brightness enhancement, and contrast improvement.

Figure 6 Comparison on U45 data sets. (A) Original image. (B) UDCP ( Drews et al., 2016 ). (C) IBLA ( Peng and Cosman, 2017 ). (D) UWCNN ( Li et al., 2020a ). (E) FUnIEGAN ( Islam et al., 2020b ). (F) Ucolor ( Li et al., 2021 ). (G) UDNet ( Saleh et al., 2022 ). (H) Proposed.

4.4 Ablation study

For the purpose of conducting a more detailed analysis of the proposed method, extensive ablation studies were performed to examine the impact of each stage of the proposed framework. This was done to demonstrate the effectiveness of each component in Zero-UAE, with a particular focus on the loss functions and training datasets.

4.4.1 Ablation study on loss functions

The outcomes produced by various combinations of loss functions are depicted in Figure 7 , where “w/o” denotes “without.”

Figure 7 Ablation study of the loss functions (underwater color adaptive correction loss L uac , illumination smoothness loss L tvδ , exposure control loss L exp , and spatial consistency loss L spa ). (A) Input. (B) Total. (C) w/o L uac . (D) w/o L TVδ . (E) w/o L exp . (F) w/o L spa .

For a direct visual comparison of the impact of loss functions on network training, only the network output results are presented. When the underwater color adaptive correction loss L uac is not considered, the underwater blue-green color tone cannot be completely eliminated, leading to potential color bias issues, such as over-enhancement of underwater environmental regions. The absence of illumination balance loss L tvδ hinders correlations between adjacent regions, resulting in noticeable artifacts and imbalanced areas in the images. Without exposure control loss L exp , underwater images may experience overexposure issues. Without spatial consistency loss L spa , underwater images may encounter issues of insufficient contrast saturation. Therefore, these several loss functions complement each other, allowing the resulting images to achieve optimal color restoration and haze removal.

Table 5 presents the average quantitative evaluation of the ablation study on UIEB, yielding the following observations: 1) The stability of our zero-shot framework is primarily governed by the losses L tvδ and L uac ; removing either significantly diminishes restoration performance. 2) Both L exp and L spa losses are not indispensable for stabilizing network training. L exp effectively controls complex underwater lighting conditions, while L spa enhances image contrast. Visual inspection indicates favorable image results, and although the inclusion of L spa leads to a slight decrease in evaluation metrics, this does not significantly impact overall perceptual quality. 3) Each loss contributes to restoring underwater images in its respective role, and the combination of all losses achieves optimal performance.

Table 5 Ablation study on UIEB dataset.

4.4.2 Impact of training data

In order to test the impact of the training dataset, Zero-UAE is retrained on different datasets: 1) the original images from the UIEB dataset ( Li et al., 2019 ) (a), 2) the original training data (b), 3) 3,700 underwater images provided by the EUVP dataset ( Islam et al., 2020b ) (c), and 4) 2,000 unlabeled underwater images from the HICID dataset ( Han et al., 2022 ) (d). As shown in Figures 8C, D , after switching to different datasets, the color bias issue in underwater images cannot be completely eliminated in Zero-UAE. For instance, if the input underwater image has a bluish tint, the resulting image will maintain the bluish tint of the input. These results indicate the rationality and necessity of using the current training dataset in the training process of our network.

Figure 8 Ablation study of the Training Data. (A) Input ( Li et al., 2019 ). (B) The results of this method. (C) EUVP Dataset. (D) HICID Dataset.

4.5 Testing runtime

To research the efficiency of the proposed model, this paper compares the average testing runtime of different methods. These comparisons help assess the speed performance of this paper’s model in processing underwater images, comparing it with other methods to validate its superiority. This is crucial for understanding the practicality and performance of the method in real-world applications. This paper selected images from the 256×256 UIEB dataset for testing. The runtimes were measured on a computer equipped with an NVIDIA RTX 3080Ti GPU and AMD Ryzen 7-5800X CPU. The average runtimes are shown in Table 6 , where “RT” represents the required runtime per image. Image quality evaluation metrics NIQE and MUSIQ are also provided for reference. The time efficiency of the proposed Zero-UAE is slightly better than that of FuniE-GAN and UDNet. Some other methods have relatively longer runtimes, requiring complex inference for each image. Additionally, our proposed method achieves the optimal metric evaluation results with the least time consumption.

Table 6 Comparison on Testing Runtime (RT) (in seconds).

5 Conclusion

This paper presents a novel lightweight zero-reference deep network for underwater image enhancement (Zero-UAE), eliminating the requirements for paired or unpaired data. The image enhancement problem is transformed into the task of estimating parameters for a curve model mapping. A set of differentiable underwater non-reference loss functions is designed to guide the network training. The method can adaptively compensate for image color and brightness to enhance visual quality. It is noteworthy that, compared to other deep learning methods, the proposed method does not require any reference images during the training process. Under zero-reference training, Zero-UAE exhibits satisfactory visual performance in brightness, color, contrast, and underwater environments. Extensive experiments on multiple benchmarks demonstrate that the proposed method outperforms state-of-the-art methods both on qualitative and quantitative evaluations. Due to these advantages, it holds significant value in practical applications such as real-time processing tasks on underwater robots in marine exploration.

In the future, our goal is to improve the generalization performance of the zero-reference network in underwater sonar image and underwater optical image processing tasks. We plan to further refine the loss functions to enhance the underwater image color restoration and uniform contrast capabilities in challenging underwater scenes. Additionally, we intend to explore the possibility of integrating additional datasets and other models to further enhance the network’s ability to preserve low-level features, thereby increasing its applicability in real-world underwater environments and contributing to underwater autonomous detection tasks.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Author contributions

TL: Conceptualization, Data curation, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. KZ: Funding acquisition, Resources, Supervision, Writing – review & editing. XW: Data curation, Investigation, Resources, Writing – review & editing. WS: Data curation, Investigation, Resources, Writing – review & editing. HW: Investigation, Supervision, Validation, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Natural Science Foundation Program of Liaoning (Grant No. 2022-KF-18-04) and the Science Research Project under grant agreement No. LJKZ-0731, which are funded by the Educational Department of Liaoning Province.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: underwater image enhancement, zero-reference, parameter estimation network, loss functions, lightweight

Citation: Liu T, Zhu K, Wang X, Song W and Wang H (2024) Lightweight underwater image adaptive enhancement based on zero-reference parameter estimation network. Front. Mar. Sci. 11:1378817. doi: 10.3389/fmars.2024.1378817

Received: 30 January 2024; Accepted: 22 March 2024; Published: 11 April 2024.

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Copyright © 2024 Liu, Zhu, Wang, Song and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Kaiyan Zhu, [email protected]

This article is part of the Research Topic

Deep Learning for Marine Science, Volume II