Deblur image that has locally varying (but well known) motion blur
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Answered: Image Analyst on 6 Apr 2022
Hi, I have an image where motion blur occurs due to long exposure photographing. I know the amount of motion blur for each pixel of the image. Is there a way to de-motion-blur this image with a locally varying de-blur algorithm?
I have attached images that show the original, motion-blurred image, and the x and y blur amount (in pixel units). The original Matlab data is located here:
Should I segement the image into smaller sub-images and then deblur each sub-image? Isn't there a nicer way...? Could you give me a hint where I can start? Thanks!!
p.s.: The dataset is not perfect as there are additional sources of image blurring too (caused by lens misalignment, lower left corner for example). But this is the best dataset I currently have.
Bjorn Gustavsson on 4 Apr 2022
Some time ago I wrote a pair of simple "variable-psf-blurring and variable-psf-deblurring" functions. They at least work OK-ish for the case where the psf-s are nearly separable into a pair of horizontal and vertical psf-s that varies smoothly over the image. For this type of task you might have to go to the litterature, or do it region-by-region from scratch. For what it is worth, I attach the varying-psf-tools.
Image Analyst on 6 Apr 2022
You might want to try BM3D. In addition to being arguably the best denoising algorithm out there, it also does deblurring. Here is an example:
The original is on the left. The blurred input image is in the middle, and the deblurred/repaired image is on the right. It looks almost like the original. You can get the MATLAB code here:
Alternatively you could try a Mean Shift Filter, a total variance deblurring, or others. Many are in the File Exchange:
Try some of them and see what works well for you. Such as
Find more on Deblurring in Help Center and File Exchange
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