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Recent Posts
- Dimensionality reduction for image and texture set compression May 21, 2020
- “Optimizing” blue noise dithering – backpropagation through Fourier transform and sorting April 26, 2020
- Bilinear texture filtering – artifacts, alternatives, and frequency domain analysis April 14, 2020
- Using JAX, numpy, and optimization techniques to improve separable image filters March 15, 2020
- Separate your filters! Separability, SVD and low-rank approximation of 2D image processing filters February 3, 2020
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Categories
Tag Archives: machine learning
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
8 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
2 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
3 Comments
