Deconvolution might sound like a term reserved for mathematicians or AI engineers, but its impact can ripple through industries far beyond its technical origins. From healthcare imaging to creative industries, it holds transformative potential. At the same time, it comes with a frustrating sidekick, the infamous checkerboard artifacts. These artifacts, while visually intriguing in some contexts, are often a headache that can compromise the credibility of image-based outputs. I’ve been pondering this juxtaposition and the implications it has for entrepreneurs and innovators across industries. Let’s explore why these concepts matter and how understanding them can give you an edge.
What Are Checkerboard Artifacts?
Checkerboard artifacts are those grid-like patterns that appear in images during certain processes, particularly when using neural networks to generate or modify visuals. They typically occur due to deconvolution, a mathematical operation used to increase the size of an image while working with convolutional neural networks (CNNs). Instead of a smooth visual output, you end up with a checkerboard-like distortion. For entrepreneurs curious about leveraging AI-based image technology, understanding these artifacts is crucial.
The root cause lies in the uneven overlap of kernel and stride configurations during upsampling via deconvolution. A bad setup in the neural network architecture can make these patterns worse, leading to outputs that feel artificial or broken. This is an issue in everything from video game visuals to machine learning models used in medical imaging, where clarity isn’t just an aesthetic concern, it’s critical.
Five Most Common Causes of Checkerboard Artifacts:
- Improper Kernel and Stride Ratios: A mismatch between the convolutional kernel size and stride leads to uneven contributions across pixels.
- Multiple Layers with Errors: Stacking several deconvolution layers compounds the distortion, amplifying checkerboard effects.
- Gradient Noise Build-Up: During training, some methods used for optimizing the model’s performance inadvertently introduce artifacts.
- Improper Feature Scaling: Without proper resizing mechanisms, like bilinear or nearest neighbor upsampling, the deconvolution introduces harsh transitions in pixel values.
- Errors in Generator Networks: For those building generative networks, employing deconvolution in the generator can often result in glossy but unrealistic patterns, exactly what checkerboards signify.
How to Solve This Issue?
Getting rid of checkerboard artifacts is not as tricky as it may initially seem. While it takes a bit of technical understanding, making the right design tweaks to processes or models is usually effective. Here are ways to tackle the problem:
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Switch to Resize-Convolve: One of the most foolproof methods is performing an intentional image resize (via bilinear or nearest-neighbor interpolation) followed by a standard convolutional operation. This approach smooths out uneven overlaps. You can find an example of how this process works in the Distill publication on Deconvolution.
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Use Kernels with Ratios Divisible by the Stride: Lowering the overlap issue can often be achieved by choosing kernel sizes that divide evenly over the stride of each layer.
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Pay Attention to Padding: When coding your convolutional layers, ensure accurate padding for balanced pixel calculations and to reduce edge-based distortions. This can often involve trial and error, even for experienced developers.
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Evaluate GAN Discriminators: Discriminators in Generative Adversarial Networks contribute to gradient flows and can exacerbate artifact visibility. Testing configurations is essential to ensure the overall system works harmoniously.
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Regularize Training: Integrate constraints or optimization methods that prevent overfitting to nonsensical visual features (like high-frequency noise).
Why It’s Relevant for Entrepreneurs
If your startup focuses on AI-based visuals, these artifacts are a red flag for users. For instance, imagine you’re creating AI tools for the art or design sector. Your clients will judge your outputs by their aesthetic quality, and visible grid-lines make outputs feel machine-made instead of finely crafted. If your portfolio includes healthcare or diagnostics, the stakes are even higher. A radiologist relying on blurry or distorted patterns due to incomplete image scaling loses trust in the product.
For startups, this is a quality-control issue as much as a technical problem. Make it part of your testing to ensure the outputs resonate with customer expectations, especially in industries where precision matters.
Pragmatic Advice: Testing for Checkerboards
It’s not difficult to spot the presence of artifacts. If you’re experimenting with image outputs from a neural network, here’s how you could test for checkerboard errors:
- Create test scenarios with high contrast images (sharp edges or stark colors tend to reveal problems more easily).
- Zoom in on patterns to check pixel overlaps. Focus on testing the upsampled results meticulously.
- Compare outputs when replacing deconvolution with resize-convolve or better-tuned configurations.
- Share the images in focus groups or with a mentor to identify distortions that may not be intuitive to notice.
Fix it before customers do; by the time a client alerts you to visible shortcomings, it harms your perception.
What We Can Learn About Startup Thinking
Aside from the technical reparations, this problem illustrates a wider lesson for founders. Over-reliance on a tool or process, without understanding the side effects, can backfire. It’s easy to look at neural networks and think, “It’s a powerful model, so the output will be clean,” much like overestimating what a tool like ChatGPT can do without tweaking your prompts properly. Whether you’re developing a new product or partnering with an AI vendor, always understand the mechanisms that drive your deliverables.
Wrapping It Up
Checkerboard artifacts are more than a design problem; they’re a case study in how small imperfections can tarnish the perception of innovative work. Modern neural networks have solutions, resize-convolve or careful configurations offer elegant fixes, but these work only when adopted intentionally. And for entrepreneurs, it’s a skill to always dig into the details. A small artifact may err the outputs for now, but fixing it can open the doors to spotless execution later.
If you’re diving deeper into how to create seamless graphical output in neural networks, explore detailed resources on deconvolution.
FAQ
1. What are checkerboard artifacts?
Checkerboard artifacts are grid-like distortions visible in images processed by neural networks, often caused by deconvolution during upsampling operations. They appear due to mismatched kernel sizes and stride configurations. Learn about checkerboard artifacts
2. How do checkerboard artifacts occur in neural networks?
These artifacts emerge from uneven overlaps in the deconvolution process, where certain pixels receive more contributions than others, resulting in distorted image outputs. Discover more in the publication Deconvolution and Checkerboard Artifacts.
3. Why do checkerboard artifacts matter in image processing?
Checkerboard artifacts can compromise the visual and technical integrity of AI applications, such as healthcare imaging and design tools, where clear outputs are crucial. Explore insights on this issue
4. What are the common causes behind checkerboard artifacts?
Five primary causes include improper kernel and stride ratios, stacking faulty deconvolution layers, gradient noise during training, incorrect feature scaling, and errors in generative network configurations.
5. What is the resize-convolve solution for artifact reduction?
The resize-convolve method involves resizing images through interpolation (e.g., bilinear or nearest-neighbor) followed by standard convolution operations to provide smoother outputs and eliminate artifacts. View the details in the Distill publication.
6. Can kernel size and stride adjustments minimize checkerboard artifacts?
Yes, choosing kernel sizes divisible by the stride significantly reduces uneven pixel contributions, which mitigates artifact generation. Check out sub-pixel convolution techniques.
7. How can padding help in fixing edge distortions?
Accurate padding ensures balanced pixel calculations during convolutional processes, resolving edge-based issues and artifact occurrences. Learn more about convolution arithmetic.
8. Are checkerboard artifacts prevalent in medical imaging?
Yes, medical imaging often relies on precision visuals, and checkerboard artifacts can compromise clarity, making reliable implementations essential. Gain insights into medical imaging challenges.
9. How does feature scaling impact artifacts?
Improper scaling mechanisms introduce harsh transitions in pixel values, increasing artifact visibility; resizing methods like bilinear interpolation are effective countermeasures.
10. What can startups learn from solving checkerboard artifacts?
Understanding critical technical details like artifact origins and fixes allows startups to improve product credibility and user satisfaction, ensuring competitive edge in industries relying on AI-based visuals. Learn more about startup strategies for AI visuals.
About the Author
Violetta Bonenkamp, also known as MeanCEO, is an experienced startup founder with an impressive educational background including an MBA and four other higher education degrees. She has over 20 years of work experience across multiple countries, including 5 years as a solopreneur and serial entrepreneur. Throughout her startup experience she has applied for multiple startup grants at the EU level, in the Netherlands and Malta, and her startups received quite a few of those. She’s been living, studying and working in many countries around the globe and her extensive multicultural experience has influenced her immensely.
Violetta Bonenkamp's expertise in CAD sector, IP protection and blockchain
Violetta Bonenkamp is recognized as a multidisciplinary expert with significant achievements in the CAD sector, intellectual property (IP) protection, and blockchain technology.
CAD Sector:
- Violetta is the CEO and co-founder of CADChain, a deep tech startup focused on developing IP management software specifically for CAD (Computer-Aided Design) data. CADChain addresses the lack of industry standards for CAD data protection and sharing, using innovative technology to secure and manage design data.
- She has led the company since its inception in 2018, overseeing R&D, PR, and business development, and driving the creation of products for platforms such as Autodesk Inventor, Blender, and SolidWorks.
- Her leadership has been instrumental in scaling CADChain from a small team to a significant player in the deeptech space, with a diverse, international team.
IP Protection:
- Violetta has built deep expertise in intellectual property, combining academic training with practical startup experience. She has taken specialized courses in IP from institutions like WIPO and the EU IPO.
- She is known for sharing actionable strategies for startup IP protection, leveraging both legal and technological approaches, and has published guides and content on this topic for the entrepreneurial community.
- Her work at CADChain directly addresses the need for robust IP protection in the engineering and design industries, integrating cybersecurity and compliance measures to safeguard digital assets.
Blockchain:
- Violetta’s entry into the blockchain sector began with the founding of CADChain, which uses blockchain as a core technology for securing and managing CAD data.
- She holds several certifications in blockchain and has participated in major hackathons and policy forums, such as the OECD Global Blockchain Policy Forum.
- Her expertise extends to applying blockchain for IP management, ensuring data integrity, traceability, and secure sharing in the CAD industry.
Violetta is a true multiple specialist who has built expertise in Linguistics, Education, Business Management, Blockchain, Entrepreneurship, Intellectual Property, Game Design, AI, SEO, Digital Marketing, cyber security and zero code automations. Her extensive educational journey includes a Master of Arts in Linguistics and Education, an Advanced Master in Linguistics from Belgium (2006-2007), an MBA from Blekinge Institute of Technology in Sweden (2006-2008), and an Erasmus Mundus joint program European Master of Higher Education from universities in Norway, Finland, and Portugal (2009).
She is the founder of Fe/male Switch, a startup game that encourages women to enter STEM fields, and also leads CADChain, and multiple other projects like the Directory of 1,000 Startup Cities with a proprietary MeanCEO Index that ranks cities for female entrepreneurs. Violetta created the "gamepreneurship" methodology, which forms the scientific basis of her startup game. She also builds a lot of SEO tools for startups. Her achievements include being named one of the top 100 women in Europe by EU Startups in 2022 and being nominated for Impact Person of the year at the Dutch Blockchain Week. She is an author with Sifted and a speaker at different Universities. Recently she published a book on Startup Idea Validation the right way: from zero to first customers and beyond, launched a Directory of 1,500+ websites for startups to list themselves in order to gain traction and build backlinks and is building MELA AI to help local restaurants in Malta get more visibility online.
For the past several years Violetta has been living between the Netherlands and Malta, while also regularly traveling to different destinations around the globe, usually due to her entrepreneurial activities. This has led her to start writing about different locations and amenities from the POV of an entrepreneur. Here’s her recent article about the best hotels in Italy to work from.