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Recent Posts
- Transforming “noise” and random variables through non-linearities March 16, 2022
- Fast, GPU friendly, antialiasing downsampling filter March 7, 2022
- Exposure Fusion – local tonemapping for real-time rendering February 28, 2022
- Light transport matrices, SVD, spectral analysis, and matrix completion February 15, 2022
- Insider guide to tech interviews January 4, 2022
Categories
Tag Archives: machine learning
Procedural Kernel (Neural) Networks
Last year I worked for a bit on a fun research project that ended up published as an arXiv “pre-print” / technical report and here comes a few paragraph “normal language” description of this work. Neural Networks are taking over … Continue reading
Posted in Code / Graphics
Tagged image processing, linear algebra, machine learning, maths, signal processing
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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
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
13 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
6 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
10 Comments
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
5 Comments
Bart Wronski 