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It's remarkable what can be done with simple filters.

The trendiest thing lately is image supersizing using neural networks. That must use far more processing power than your approach. Is this overkill? A different approach for a different use.

Randomly Googled link, in case anyone is curious: https://openaccess.thecvf.com/content_cvpr_2017_workshops/w1...

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Right. Fundamentally, Magic Kernel Sharp seeks to be an efficient but good approximation to a rectangular low-pass filter; it doesn't add information, and ideally doesn't destroy or distort it either.

Yes, I have seen other methods for guessing or dropping in extra detail. (Indeed, my old "UnBlur" method of deblurring effectively makes a best guess as to what an image looked like before it was blurred, which in the presence of noise is always a selection of one particular possible solution.) There's nothing wrong with that — it's just a different operation.

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