Tag Archives: jax

Transforming “noise” and random variables through non-linearities

Posted on March 16, 2022 by bartwronski

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

Posted on March 7, 2022 by bartwronski

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

Posted on July 20, 2021 by bartwronski

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)

Posted on April 21, 2021 by bartwronski

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

Posted on April 26, 2020 by bartwronski

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

Posted on March 15, 2020 by bartwronski

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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