Tag Archives: signal processing

Gradient-descent optimized recursive filters for deconvolution / deblurring

This post is a follow-up to my post on deconvolution/deblurring of the images. In my previous blog post, I discussed the process of “deconvolution” – undoing a known convolution operation. I have focused on traditional convolution filters – “linear phase, … Continue reading

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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

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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 “pre-print” / technical report and here comes a few paragraph “normal language” description of this work. Neural Networks are taking over … Continue reading

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Study of smoothing filters – Savitzky-Golay filters

Last week I saw Daniel Holden tweeting about Savitzky-Golay 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

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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

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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

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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

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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

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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

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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

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