|Stochastic Systems Group|
Removing photographic blur caused by camera motion
Camera shake during exposure leads to objectionable image blur and ruins many photographs. Conventional blind deconvolution methods typically assume frequency domain constraints on images, or overly simplied parametric forms for the motion path during camera shake. Real camera motions can follow convoluted paths, and a spatial domain prior can better maintain visually salient image characteristics. We introduce a multi-scale method to remove the effects of camera shake from seriously blurred images, by estimating the most probable blur and original image using a variational approximation to the posterior probability, and assuming a heavy-tailed distribution for bandpassed image statistics. Our method assumes a uniform camera blur over the image, negligible in-plane camera rotation, and no blur caused by moving objects in the scene. The algorithm operator specifies an image region without saturation effects within which to estimate the blur kernel. I'll discuss issues in this blind deconvolution problem, and show results for a variety of digital photographs.
Invitation to submit examples: I invite audience members to submit examples of motion-blurred photographs to me a few days ahead of time. I'll show the images you submit, and the result of our algorithm applied to them. If you have a favorite blind deconvolution or restoration algorithm, please apply it to your image and send it and I'll show that, too.
Joint work with: Rob Fergus, Barun Singh, both from MIT CSAIL, and Aaron Hertzman and Sam Roweis, both from the University of Toronto.
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