Tag Archives: jax

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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Progressive image stippling and greedy blue noise importance sampling

Introduction I recently read the “Gaussian Blue Noise” paper by Ahmed et al. and was very impressed by the quality of their results and the rigor of their method. They provide a theoretical framework to analyze the quality of blue … Continue reading

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Transforming “noise” and random variables through non-linearities

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

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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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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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Superfast void-and-cluster Blue Noise in Python (Numpy/Jax)

This is a super short blog post to accompany this Colab notebook. It’s not an official part of my dithering / Blue Noise post series, but thematically fits it well and be sure to check it out for some motivation … Continue reading

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“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

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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

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