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Abstract:
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Observed images are often blurred. Blind image deblurring is for estimating a true image from its observed but blurred version, when the blurring mechanism described by a point spread function is not completely specified beforehand. This is a challenging "ill-posed" problem, because (i) theoretically speaking, the true image can not be uniquely determined by the observed image when the point spread function is unknown, and (ii) practically, observed images often contain noise and the noise would bring numerical instability to the image deblurring problem. We propose a blind image deblurring methodology without restrictive assumptions on the point spread function or the true image. Our method pays special attention to regions around step and roof/valley edges when deblurring. Theoretical justifications and numerical studies show that it works well in applications.
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