-
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 – data-driven nonlinear dimensionality reduction May 30, 2021
- Superfast void-and-cluster Blue Noise in Python (Numpy/Jax) April 21, 2021
Categories
Tag Archives: linear algebra
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
10 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
