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Restoration of defocused and blurred images (2012)

yuzhikov.com

41–48 of 48 posts

Re: Restoration of defocused and blurred images (2012)

#41
post #29

Note that this works for out of focus photos, not for enlarging tiny details of in focus photos. They may look similarly blurry, but are mathematically very different.

How's that? Can't you think of a pixel as the average value of it's subpixels (=blur)?

"Subpixels" are an artifact of the display type you're using. Although there may be individual colour components of an image pixel, they aren't restricted to a region of that pixel. And in most digital images - those made with a colour filter array, as opposed to a multi-chip, multi-shot, or Foveon-type sensor - the colours at each individual pixel are just an educated guess based on the surrounding pixel values; the RGB values are not captured separately, and attempting to increase resolution depending on which part of your screen is lit up by how much in order to render the colour that should appear at the corresponding pixel can only introduce artifacts. (If green is brighter than red in a particular pixel, it only means that green is brighter than red, not that the detail is laterally displaced.)

Re: Restoration of defocused and blurred images (2012)

#42
post #25

Earlier quoted context omitted.

It wasn't too clear but I wonder if the author was referring to deconvolution under a Total Variation prior -- this is a little different to deconvolving and then applying TV denoising or just applying TV denoising. Either way, the results of overdoing it with TV are the same: cartoony images with large regions of constant colour. The difference is that incorporating TV within iterative deconvolution reduces some com…

I agree that the staircasing effect is definitely the biggest drawback of Total Variation. In the "Smoothed" picture the noise is removed but the results are blocky. The first way to deal with it is to take into account higher powers of the differences, e.g. using a linear combination p-norms or a Huber function. The second way is to take into account second order differences. This promotes piecewise affine instead o…

> Other than being more complex, the biggest downside is that all of these methods have some new parameter(s) to tune.

They're so fast to run though, that just doing warm-starts and a huge solution path (or grid in the case of additional penalties) with a BIC selection criteria is a pretty decent way to auto-tune the parameters.

Re: Restoration of defocused and blurred images (2012)

#44
I'm author of this article (and SmartDeblur http://smartdeblur.net/), thank you for posting!

Also, you can look at the second part that describes practical issues and their solutions: http://yuzhikov.com/articles/BlurredImagesRestoration2.htm And if you have any qustions, feel free to ask me.

Re: Restoration of defocused and blurred images (2012)

#46
post #13

This is a very old link. Same feature has since landed in Photoshop

And Photoshop is expensive and is now rent-ware, which means Photoshop is a non-starter, so the feature might as well not exist for me.

I've been looking for this article ever since I saw the link the first time it was posted, so I am glad to see it! Plus, it's interesting, and you get source code (unlike Photoshop)

Re: Restoration of defocused and blurred images (2012)

#48

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...

That looks really useful. Could you provide a link to your/an implementation?
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