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Recent Posts
- Study of smoothing filters – Savitzky-Golay filters November 3, 2021
- Practical Gaussian filtering: Binomial filter and small sigma Gaussians October 31, 2021
- Modding Korg MS-20 Mini – PWM, Sync, Osc2 FM October 14, 2021
- Processing aware image filtering: compensating for the upsampling July 20, 2021
- Comparing images in frequency domain. “Spectral loss” – does it make sense? July 6, 2021
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Category Archives: Code / Graphics
Study of smoothing filters – Savitzky-Golay filters
Last week I saw Daniel Holden tweeting about Savitzky-Golay filters and their properties (less smoothing than a Gaussian filter) and I got excited… because I have never heard of them before and it’s an opportunity to learn something. When I … Continue reading
Posted in Code / Graphics
Tagged algorithms, digital signal processing, image processing, maths, python, signal processing
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Practical Gaussian filtering: Binomial filter and small sigma Gaussians
Gaussian filters are the bread and butter of signal and image filtering. They are isotropic and radially symmetric, filter out high frequencies extremely well, and just look pleasant and smooth. In this post I will cover two of my favorite … Continue reading
Processing aware image filtering: compensating for the upsampling
This post summarizes some thoughts and experiments on “filtering aware image filtering” I’ve been doing for a while. The core idea is simple – if you have some “fixed” step at the end of the pipeline that you cannot control … Continue reading
Posted in Code / Graphics
Tagged algorithms, digital signal processing, filtering, graphics, image processing, jax, postprocessing, signal processing
5 Comments
Comparing images in frequency domain. “Spectral loss” – does it make sense?
Recently, numerous academic papers in the machine learning / computer vision / image processing domains (re)introduce and discuss a “frequency loss function” or “spectral loss” – and while for many it makes sense and nicely improves achieved results, some of … Continue reading
Posted in Code / Graphics
Tagged algorithms, image processing, linear algebra, machine learning, maths, neural networks
3 Comments
Neural material (de)compression – data-driven nonlinear dimensionality reduction
In this post I come back to something I didn’t expect coming back to – dimensionality reduction and compression for whole material texture sets (as opposed to single textures) – a significantly underexplored topic. In one of my past posts … Continue reading
Superfast void-and-cluster Blue Noise in Python (Numpy/Jax)
This is a super short blog post to accompany this Colab notebook. It’s not an official part of my dithering / Blue Noise post series, but thematically fits it well and be sure to check it out for some motivation … Continue reading
Posted in Code / Graphics
Tagged blue noise, dithering, graphics programming, jax, numpy, python
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Computing gradients on grids of pixels and voxels – forward, central, and… diagonal differences
In this post, I will focus on gradients of image signals defined on grids in computer graphics and image processing. Specifically, gradients / derivatives of images, height fields, distance fields, when they are represented as discrete, uniform grids of pixels … Continue reading
Bilinear down/upsampling, aligning pixel grids, and that infamous GPU half pixel offset
It’s been more than two decades of me using bilinear texture filtering, a few months since I’ve written about bilinear resampling, but only two days since I discovered a bug of mine related to it. 😅 Similarly, just last week … Continue reading
Posted in Code / Graphics
Tagged digital signal processing, gpu, image processing, libraries, numpy, sampling, signal processing, upsampling
9 Comments
Is this a branch?
Let’s try a new format – “shorts”; small blog posts where I elaborate on ideas that I’d discuss at my twitter, but they either come back over and over, or the short form doesn’t convey all the nuances. I often … Continue reading
Posted in Code / Graphics
Tagged assembly, cpu, gpu, optimizations, profiling, programming, simd
6 Comments
Why are video games graphics (still) a challenge? Productionizing rendering algorithms
Intro This post will cover challenges and aspects of production to consider when creating new rendering / graphics techniques and algorithms – especially in the context of applied research for real time rendering. I will base this on my personal … Continue reading
Compressing PBR material texture sets with sparsity and k-SVD dictionary learning
Introduction In this blog post, I am going to continue exploration of compressing whole PBR texture sets together (as opposed to compressing each texture from the set separately) and using the fact that those textures are strongly correlated. In my … Continue reading
Posted in Code / Graphics
Tagged compression, graphics, graphics programming, image processing, linear algebra, machine learning, maths, PBR, rendering, signal processing, textures
6 Comments
Dimensionality reduction for image and texture set compression
In this blog post I am going to describe some of my past investigations on reducing the number of channels in textures / texture sets automatically and generally – without assuming anything about texture contents other than correspondence to some … Continue reading
Posted in Code / Graphics
Tagged compression, graphics, image processing, linear algebra, machine learning, maths, PBR, physically-based shading, textures
11 Comments
“Optimizing” blue noise dithering – backpropagation through Fourier transform and sorting
Introduction This will be a blog post that is second in an (unanticipated) series on interesting uses of the JAX numpy autodifferentiation library, as well as an extra post in my very old post series on dithering in games and … Continue reading
Posted in Code / Graphics
Tagged blue noise, dithering, frequency domain, image processing, jax, machine learning, noise, numpy, python
4 Comments
Bilinear texture filtering – artifacts, alternatives, and frequency domain analysis
In this post we will look at one of the staples of real-time computer graphics – bilinear texture filtering. To catch your interest, I will start with focusing on something that is often referred to as “bilinear artifacts”, trapezoid/star-shaped artifact … Continue reading
Posted in Code / Graphics
Tagged blur, filtering, image processing, postprocessing, temporal, temporal supersampling
11 Comments
Using JAX, numpy, and optimization techniques to improve separable image filters
In today’s blog post I will look at two topics: how to use JAX (“hyped” new Python ML / autodifferentiation library), and a basic application that is follow-up to my previous blog post on using SVD for low-rank approximations and … Continue reading
Posted in Code / Graphics
Tagged bokeh, colab, github, graphics programming, image processing, jax, machine learning, maths, numpy, postprocessing, programming, python
9 Comments
Separate your filters! Separability, SVD and low-rank approximation of 2D image processing filters
In this blog post, I explore separable convolutional image filters: how can we check if a 2D filter is separable, and how to compute separable approximations to any arbitrary 2D filter represented in a numerical / matrix form using SVD. Continue reading
Posted in Code / Graphics
Tagged algorithms, approximation, blur, bokeh, depth of field, graphics, image processing, linear algebra, numpy, optimizations, postprocessing, python
9 Comments
Analyze your own activity data using Google Takeout – music listening stats example
The goal of this post is to show how to download our own data stored and used by internet services to generate personalized stats / charts like below and will show step-by-step how to do it using colab, Python, pandas, … Continue reading
Posted in Code / Graphics
Tagged colab, data, numpy, programming, python, statistics, visualization
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Local linear models and guided filtering – an alternative to bilateral filter
Intro In this blog post I am going to describe an alternative tool for the graphics and image processing programmers’ toolbox – guided filtering. Guided filtering is a really handy tool that I learned about from my coworkers, and I … Continue reading
Posted in Code / Graphics
Tagged bilateral, graphics, image processing, machine learning, postprocessing, python, signal processing, ssao, upsampling
4 Comments
How (not) to test graphics algorithms
Intro Siggraph 2019 is sadly over, but as always I came back super inspired and grateful for meeting many friends. Conferences are mostly not about seeing the presentations – but about all the interesting and inspiring discussions, and one of … Continue reading
Posted in Code / Graphics
Tagged code design, code style, graphics, graphics programming, programming, siggraph, testing, tests
4 Comments
Checkerboard rendering, rotated anti-aliasing and grid frequencies
This post is inspired by three interesting statements or questions that I heard and are interconnected by non-obvious theory: First was regarding so called checkerboard rendering (example1 example2) – what is advantage of using it? After all it’s the same as … Continue reading
Posted in Code / Graphics
Tagged antialiasing, fourier, frequency domain, sampling, signal theory, supersampling
5 Comments
Bart Wronski 
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