Median filtering (2022)

Had a lot of fun implementing the Huang histogram approach and the constant time approach by hand in a prior role.

Never saw the binary tree approach. And this article, being written in summer of 22, missed out on the 2D wavelet approach published later on that year.

https://cgenglab.github.io/en/publication/sigga22_wmatrix_me....

Median filtering with large kernel can be pretty ugly as it gives pixels who are far away the same weight. I prefer weighting them (for example with a gaussian) and using a weighted histogram, use the weighted median value (interpolated between two values). It gives a result even more interesting (for some uses) than a simple gaussian blur and you can also configure the weights to make it edge preserving like a bilateral blur but better

  There is also no way to split up the median computation

What does this mean here ? Seems like we could have a rolling window by adding and subtracting pixels on the way. I’ve coded this before, although it’s not O(1) like the algorithm described at the end

I've found sorting-network-based filters to be faster than these methods up to size 25x25 or so. It has worse computational complexity, but filters up to that size covers a lot of ground in practice. See Figure 10 in https://andrew.adams.pub/fast_median_filters.pdf

I hacked together an SVG-filter based median filter once. It was horribly inefficient, but it worked: https://codepen.io/mullany/pen/ngJWvx

For a second, I thought median filtering was going to be about riding motorcycles down the median of a road to filter through traffic.....

Does anyone know of a pytorch differentiable filter that does the same thing?