"In today’s article, we’ll build a rudimentary blur algorithm and then pick it apart."

Emphasis mine. Quote from the beginning of the article.

This isn't meant to be a textbook about blurring algorithms. It was supposed to be a demonstration of how what may seem destroyed to a causal viewer is recoverable by a simple process, intended to give the viewer some intuition that maybe blurring isn't such a good information destroyer after all.

Your post kind of comes off like criticizing someone for showing how easy it is to crack a Caesar cipher for not using AES-256. But the whole point was to be accessible, and to introduce the idea that just because it looks unreadable doesn't mean it's not very easy to recover. No, it's not a mistake to be using the Caesar cipher for the initial introduction. Or a dead-simple one-dimensional blurring algorithm.

If you have an endless pattern of ..., -1, 1, -1, 1, -1, 1, ... and run box blur with a window of 2 or 4, you get ..., 0, 0, 0, 0, 0, 0, ... too.

Other than that, you're not wrong about theoretical Gaussian filters with infinite windows over infinite data, but this has little to do with the scenario in the article. That's about the information that leaks when you have a finite window with a discrete step and start at a well-defined boundary.

Interesting...I've used moving averages not thinking too hard about the underlying implications. Do you recommend any particular book or resource on DSP basics for the average programmer?

> Sorry but this post is the blind leading the blind, pun intended. Allow me to explain, I have a DSP degree.

FWIW, this does not read as constructive.