Description: Technology, programming, art, machine learning, image and signal processing
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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, finite impulse response,” the type of convolutional filter you typically think of in graphics or machine learning. Symmetric, every pixel is processed independently, very fast on GPUs.
However, If you have worked with signal processing and learned about more general digital filters, especially in audio – you might have wondered about an omission. I didn’t cover one of the common approaches to filtering signals – recurrent (“infinite impulse response”) filtering . While in graphics those techniques are not very popular, they reappear in literature, often referenced in “generalized sampling” frameworks .
Jon Barron