The hottest Image Processing Substack posts right now

And their main takeaways
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Top Technology Topics

It's all a blur

lcamtuf’s thing • 11631 implied HN points • 06 Feb 26
🕹 Technology Image Processing
  1. Averaging-based blurs are linear and often reversible, so knowing the filter and padding lets you set up simple equations to recover original pixels.
  2. A right-aligned moving average makes iterative reconstruction straightforward and can reveal fine detail even with large blur windows, though 8-bit quantization adds visible noise.
  3. Two-pass (X then Y) blurs can still be inverted if the filter biases the current pixel, and recovered images can survive normal lossy formats like JPEG unless compression is very heavy.

See it with your lying ears

lcamtuf’s thing • 6938 implied HN points • 10 Jan 26
🕹 Technology Image Processing
  1. Images and audio are both sampled data so you can apply similar transforms to both, but ears and eyes perceive artifacts very differently so the same operation can look fine and sound awful.
  2. Pixelating or reducing bit depth in audio creates stair-step or high-frequency errors that produce metallic squeals or hiss, and those artifacts are typically removed with lowpass/rolling-average filtering or proper DAC anti-aliasing.
  3. Frequency-domain editing works well if you process short, overlapping windows with a Hann (sin^2) weighting and 50% overlap so the attenuations cancel out, avoiding clicks and enabling effects like pitch shifting and vocoding.

Now I Really Won That AI Bet

Astral Codex Ten • 30146 implied HN points • 08 Jul 25
🕹 Technology Image Processing
  1. In 2022, a bet was made on whether AI could create complex images by 2025. The challenge was to generate images that matched detailed prompts.
  2. Over the years, various AI models were tested, and the results showed both progress and limitations. Improvements were made, but some details were still missed.
  3. By June 2025, an updated AI model finally met all the conditions of the bet, showing that AI can achieve a high level of image generation based on specific instructions.

Integer programming easily encloses horse

DYNOMIGHT INTERNET NEWSLETTER • 968 implied HN points • 15 Jan 26
🕹 Technology Image Processing
  1. The horse-enclosure puzzle can be encoded as an integer program using binary variables for walls and for whether a tile can escape, with linear constraints that enforce adjacency and boundaries, so solvers can quickly find and certify optimal enclosures.
  2. Integer programming is a hugely practical and powerful tool for discrete optimization: even though it’s NP-hard in theory, modern solvers solve many real-world instances very fast and reliably.
  3. Whether a combinatorial problem is fun depends on legibility and the right level of difficulty, and many NP-complete problems can be made engaging with a good interface; it’s not obvious whether this specific puzzle is provably NP-complete.

Don’t Ride This Bike! Generative AI’s persistent trouble with compositionality and parts

Marcus on AI • 3952 implied HN points • 08 Dec 24
🕹 Technology Image Processing
  1. Generative AI struggles with understanding complex relationships between objects in images. It sometimes produces physically impossible results or gets details wrong when asked to create images from text.
  2. Recent improvements in AI models, like DALL-E3, show only slight progress in handling specifications related to parts of objects. It can still mislabel parts or fail to follow more complex requests.
  3. AI systems need to improve their ability to check and confirm that generated images match the prompts given by users. This may require new technologies for better understanding between language and visuals.
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How old is this newspaper?

Pea Bee • 183 implied HN points • 29 Dec 25
🕹 Technology Image Processing
  1. PressGuessr is a game that asks players to guess the publication year of Indian Express front pages using visual and textual clues.
  2. The dataset has over 13,000 front pages from 1932–2025 gathered from Google News Archive and PressReader, with publication dates programmatically blurred and many modern full-page ads removed.
  3. Building the game was enjoyable and it’s more challenging to play than expected, and you can try it at pressguessr.com.

OpenAI Vision

Tribal Knowledge • 19 implied HN points • 20 Jun 24
🕹 Technology Image Processing
  1. Working with image processing technology can involve complex math but can also lead to practical and interesting projects like a Magic: The Gathering card detector.
  2. Reflecting on past coding projects can show growth in understanding software systems and the evolution of one's skills over time.
  3. Advancements in AI, like OpenAI's Vision API, have made tasks like image processing more accessible to engineers without the need for in-depth domain knowledge, offering a quicker way to experiment and validate ideas.

Faster LLMs, Safer Chains of Thought, and Image Tokenization Reinvented

ppdispatch • 2 implied HN points • 18 Jul 25
🕹 Technology Image Processing
  1. There's a new book that helps people understand deep learning in a clear way. It covers important topics like neural networks and how they work.
  2. A new technique called Chain-of-Thought Monitorability may help keep AI safe by watching how AI reasons with language. But it’s still seen as a bit weak and needs more work.
  3. Researchers found that recent improvements in AI reasoning might not be genuine. They suggest that better ways to check AI's performance are needed to ensure it really understands and isn't just memorizing data.

AprilTags: Why Robotics Invented Its Own QR Code

Luminotes • 7 implied HN points • 09 Feb 24
🕹 Technology Image Processing
  1. AprilTags are similar to QR codes but are used as fiducial markers in robotics for localization purposes.
  2. AprilTags, created by the reputable robotics lab April, enable systems to localize features in 6 degrees of freedom using a single image.
  3. AprilTags differ from QR codes as they are designed for easy detection in low resolution, unevenly lit, or cluttered images and can detect multiple tags.

A Nibble of Quadtrees in Rust

Get Code • 7 implied HN points • 22 Feb 23
🕹 Technology Image Processing
  1. Quadtrees are data structures where each non-leaf node has exactly four children and are used to represent properties of two-dimensional space.
  2. Quadtrees are used for performance reasons, like optimizing collision detection in simulations with many moving objects.
  3. Implementing region quadtrees in Rust involves subdividing the tree based on error thresholds and region lengths to efficiently represent images.

Generative A-Eye #11 - 2nd/3rd Oct,2024

Martin’s Newsletter • 0 implied HN points • 03 Oct 24
🕹 Technology Image Processing
  1. New methods are emerging in AI image editing, like Gaussian Splatting, which allows users to manipulate image selections in 3D space. This makes it easier to edit images in more creative ways.
  2. Researchers are exploring how to improve text-to-image generation by enhancing data augmentation techniques and exploring token lengths in models. These advancements aim to make AI-generated images more realistic and of higher quality.
  3. There are important discussions around the robustness of AI-generated image detectors, as generative AI can be misused. It's key for these detectors to adapt and respond to new challenges from ever-evolving technologies.

GraphicsMagick: The Perfect Tool for Seamless Image Processing

Curious Devs Corner • 0 implied HN points • 14 Jul 24
🕹 Technology Image Processing
  1. GraphicsMagick is a powerful tool for editing images through the command line. It can handle tasks like resizing, adding watermarks, and simulating effects such as oil painting.
  2. You can create animations and enhance images by adjusting brightness and colors using simple commands. This makes it easy to customize your images quickly.
  3. GraphicsMagick allows for task automation with shell scripts, meaning you can process multiple images at once without doing each step manually. This saves a lot of time.

The Path To Undestand Image Generation and Stable Diffusion

The Beep • 0 implied HN points • 07 Apr 24
🕹 Technology Image Processing
  1. Stable diffusion has made a big splash in image generation, allowing users to create impressive images using text prompts.
  2. Generative models like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) help in building these image generation systems by learning from existing data.
  3. Understanding how stable diffusion combines text and image decoding can enhance the image creation process, making it more flexible for various tasks.

How to Design Software — Image Uploaders

Joseph Gefroh • 0 implied HN points • 19 Oct 19
🕹 Technology Image Processing
  1. When designing a system for image uploading, it's important to consider technical concerns such as displaying, authorizing, validating, processing, storing, and associating the images.
  2. Tradeoffs to think about include scaling to handle large uploads efficiently, ensuring security to prevent vulnerabilities, managing authorization based on business logic, and maintaining consistency in the image uploading workflow.
  3. A well-designed image uploading system should support creating and using various image variants, offloading processing to separate services, ensuring consistent growth across subsystems, and establishing clear architectural boundaries for scalability.

So you want to code a convolution in Python - a bite-sized image processing with kernel

Curiosity-driven AI/ML Research Engineering • 0 implied HN points • 16 Feb 24
🕹 Technology Image Processing
  1. Images are represented as pixels, each containing information about red, green, and blue colors (RGB) within the range of 0 to 255.
  2. Implementing a convolution in Python involves using NumPy arrays and Pillow to manipulate images effectively.
  3. Convolution implementation requires traversing the image pixel by pixel, extracting image slices, computing new pixel values using kernels, and ensuring to handle all three color channels in the output.

flyswot

machinelearninglibrarian • 0 implied HN points • 22 Dec 21
🕹 Technology Image Processing
  1. The project aims to use computer vision to find and correct mislabeled images in a library's digitized manuscript collection. This will help ensure that images are accurately categorized for future use.
  2. A command line tool called 'flyswot' has been developed to check images for fake labels based on specific filename patterns. This tool helps automate the identification process.
  3. Throughout the project, important lessons were learned about practical machine learning deployment, such as dealing with domain drift and using data version control effectively.

DeOldify.NET

Barn Lab • 0 implied HN points • 07 Jun 23
🕹 Technology Image Processing
  1. Colorization of black-and-white images involves using color spaces like Lab to represent colors digitally
  2. Neural networks have been trained on colorized image datasets to aid in the colorization process
  3. DeOldify.NET offers a user-friendly way to colorize old images using AI without needing complex tools or specialized websites