
Recent Posts
 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
 On leaving California and the Silicon Valley June 28, 2021
 Neural material (de)compression – datadriven nonlinear dimensionality reduction May 30, 2021
 Superfast voidandcluster Blue Noise in Python (Numpy/Jax) April 21, 2021
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Tag Archives: maths
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 – datadriven 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 kSVD 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, physicallybased shading, textures
10 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 followup to my previous blog post on using SVD for lowrank 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
Separable disklike depth of field
This is a short note accompanying shadertoy: https://www.shadertoy.com/view/lsBBWy . It is direct implementation of “Circularly symmetric convolution and lens blur” by Olli Niemitalo (no innovation on my side, just a toy implementation) and got inspired by Kleber Garcia’s Siggraph 2017 presentation “Circular … Continue reading
Posted in Code / Graphics
Tagged bokeh, depth of field, dof, far cry 4, Gaussian, maths, photography, poisson, postprocessing, separable, witcher 2
2 Comments
Dithering part three – real world 2D quantization dithering
In previous two parts of this blog post miniseries I described basic uses mentioned blue noise definition, referenced/presented 2 techniques of generating blue noise and one of many general purpose highfrequency lowdiscrepancy sampling sequences. In this post, we will look … Continue reading
Posted in Code / Graphics
Tagged bayer, blue noise, dithering, fourier, graphics, graphics programming, interleaved gradient noise, mathematica, mathematics, maths, noise, programming, sampling
7 Comments
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