-
Recent Posts
- 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
- Computing gradients on grids of pixels and voxels – forward, central, and… diagonal differences February 28, 2021
- Bilinear down/upsampling, aligning pixel grids, and that infamous GPU half pixel offset February 15, 2021
- Is this a branch? January 18, 2021
Categories
Tag Archives: image processing
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
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
7 Comments
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
“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
3 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
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, maths, numpy, postprocessing, programming, python
7 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
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
