Restoration of defocused and blurred images (2012)

Well, yes and no. I had exactly the same thought as I was reading the article, but the bit about noise is important: in the presence of even a tiny bit of noise, the de-blur algorithm collapses pretty rapidly. And the "enhance!" scenes in TV and movies are of images that are likely to be noisy.

They're also often un-detailed due to pixelation/low resolution as much as from blur, which violates this algorithm's assumptions, so that would be another reason that "click enhance" wouldn't work.

FYI: The magic kernel isn't taken nearly as seriously by people in the field of signal processing (almost my field). I'm always surprised as to why it keeps popping up.

One of the many articles on the internet explaining why the magic kernel isn't really that magic:

http://cbloomrants.blogspot.com/2011/03/03-24-11-image-filte...

Many aspects of CV and signal processing in general produce almost magical results. It's a little less magical when you think back to stats examples of fitting a curve to some signal + (known) noise process. For linear models, the noise term can get really bad before simple regression won't persevere. Least squares in one form or another finds lots of use in image processing.

Images are just functions, and almost all of the mathematical techniques that you would use to analyze one domain hold for the other. Certain perturbations such as camera jitter are easier to deal with as "undoing" them is tantamount to assuming some underlying regularity/structure on the signal and filtering accordingly. Others, such as removing an occlusion, are harder. Humans do it well thanks to our power of inference, learned from the litany of visual examples that we take in over the course of a lifetime. It's not trivial getting an algorithm to visualize what should have been behind the person who photobombed your insta, but we do it somewhat naturally.

For occlusions and finding relationships between partially overlapping scenes, really interesting things are happening with deep learning. For noisy images, techniques continue to improve. Compressed sensing and signal recovery is an active area of research that's already paid huge dividends in many fields, especially medical imaging. I can't wait to see what becomes possible in the next five years. And, as has been noted - this article is dated. There are already more powerful techniques using deep learning for image super resolution and deblurring.

Law enforcement have large libraries of images of child sexual abuse. Sometimes the abusers appear in the photo but blur their faces. There's probably some work happening to identify those abusers.

Here's one famous example https://en.wikipedia.org/wiki/Christopher_Paul_Neil

There's a bunch of other image processing stuff that can be done. Identifying location from sparse clues is important. Identifying wall paper patterns, or coca-cola bottle labels gives clues.

I've used Marziliano's[1] blur metric to reject images with motion blur recently. It is very fast and quite accurate in distinguishing blurred and non-blurred images.

[1] http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.7.9...