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GR8K

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Here are the actual research papers for this video:

http://people.csail.mit.edu/sparis/#publi2011

- Blur kernel estimation using the Radon Transform http://people.csail.mit.edu/sparis/publi/2011/cvpr_radon/Cho...

- Modeling and Removing Spatially-Varying Optical Blur http://people.csail.mit.edu/sparis/publi/2011/iccp_blur/Kee_...

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Fascinating stuff --- thanks for digging that up. The main question in methods like this is how to constrain the blur kernel and the original image. I've only skimmed the first one, but the key idea used there seems to be to constrain things by looking at the edges in the image. The image has its own, natural distribution of edges, but there are also artificial edges created by the blurring that can serve as a clues for the motion of the camera (imagine if everything is blurred diagonally --- there will probably be a lot of artificial diagonal edges)

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