
Recent Posts
 Removing blur from images – deconvolution and using optimized simple filters May 26, 2022
 Transforming “noise” and random variables through nonlinearities March 16, 2022
 Fast, GPU friendly, antialiasing downsampling filter March 7, 2022
 Exposure Fusion – local tonemapping for realtime rendering February 28, 2022
 Light transport matrices, SVD, spectral analysis, and matrix completion February 15, 2022
Categories
Tag Archives: image processing
Transforming “noise” and random variables through nonlinearities
This post covers a topic slightly different from my usual ones and something I haven’t written much about before – applied elements of probability theory. We will discuss what happens with “noise” – a random variable – when we apply … Continue reading
Posted in Code / Graphics
Tagged blue noise, graphics, graphics programming, image processing, jax, mathematics, maths, noise, programming, python
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Fast, GPU friendly, antialiasing downsampling filter
In this shorter post, I will describe a 2X downsampling filter that I propose as a “safe default” for GPU image processing. It’s been an omission on my side that I have not proposed any specific filter despite writing so … Continue reading
Posted in Code / Graphics
Tagged filtering, graphics, graphics programming, image processing, jax, postprocessing, signal processing
3 Comments
Exposure Fusion – local tonemapping for realtime rendering
In this post I want to close the loop and come back to the topic I described ~6y ago! Local tonemapping (I’ll refer to it as LTM) – a component I considered a missing piece in video games rendering, especially … Continue reading
Posted in Code / Graphics
Tagged bilateral, graphics, graphics programming, image processing, postprocessing, tonemapping
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Procedural Kernel (Neural) Networks
Last year I worked for a bit on a fun research project that ended up published as an arXiv “preprint” / 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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Study of smoothing filters – SavitzkyGolay filters
Last week I saw Daniel Holden tweeting about SavitzkyGolay filters and their properties (less smoothing than a Gaussian filter) and I got excited… because I have never heard of them before and it’s an opportunity to learn something. When I … Continue reading
Posted in Code / Graphics
Tagged algorithms, digital signal processing, image processing, maths, python, signal processing
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Practical Gaussian filtering: Binomial filter and small sigma Gaussians
Gaussian filters are the bread and butter of signal and image filtering. They are isotropic and radially symmetric, filter out high frequencies extremely well, and just look pleasant and smooth. In this post I will cover two of my favorite … Continue reading
Processing aware image filtering: compensating for the upsampling
This post summarizes some thoughts and experiments on “filtering aware image filtering” I’ve been doing for a while. The core idea is simple – if you have some “fixed” step at the end of the pipeline that you cannot control … Continue reading
Posted in Code / Graphics
Tagged algorithms, digital signal processing, filtering, graphics, image processing, jax, postprocessing, signal processing
7 Comments
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
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
11 Comments
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
17 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
Bilinear texture filtering – artifacts, alternatives, and frequency domain analysis
In this post we will look at one of the staples of realtime 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/starshaped artifact … Continue reading
Posted in Code / Graphics
Tagged blur, filtering, image processing, postprocessing, temporal, temporal supersampling
12 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
10 Comments
Separate your filters! Separability, SVD and lowrank 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
14 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
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