Bart Wronski: Recent Episodes
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We all know how the internet works—lots of “hot takes,” polarizing opinions, trolling, and ignorance. Recently, everyone has opinions on AI and LLMs/GenAI in particular. I won’t focus here on “gold rush” influencers, bad grifters, people who build their business … Continue reading →
Intro I love unsophisticated solutions with minimal dependencies. There is a reason the blog you are reading right now is on wordpress.com and ugly – the friction from a post idea and starting to write it to finally publishing a … Continue reading →
Intro Around the end of March 2021, I was finishing packing and ready to leave my home of almost five years… the (in)famous Silicon Valley moving to New York City. This was a well-thought-out and long-term decision (rare for me; … Continue reading →
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 →
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 →
In this post, we’ll have a look at the idea of removing blur from images, videos, or games through a process called “deconvolution”. We will analyze what makes the process of deblurring an image (blurred with a known blur kernel) … Continue reading →
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 →
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 →
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 →
In this post I’ll describe a small hike into the landscape of using linear algebra methods for analyzing seemingly non-algebraic problems, like light transport. This is very common in some domains of computer science / electrical engineering (seeing everything as … Continue reading →
I’ve been meaning to write this post for over a year… an unofficial insider guide to tech interviews! This is the essence of the advice I give my friends (minus personal circumstances and preferences) all the time, and I figured … Continue reading →
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 →
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 →
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 →
Almost every graphics programmer I know is playing right now with some electronics (virtual high five!), so I won’t be any different. But I decided to make it “practical” and write something that might be useful to others – how … Continue reading →
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 →
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 →
Beginning of the year, my wife and I made a final call – to leave California, “as soon as possible”. To be fair, we talked about this for a long time; a few years, but without any concrete date or … Continue reading →
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 →
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 →