In this paper, we propose a method to restore a single image affected by space-varying blur. The main novelty of our method is the use of recurring patterns as regularization during the restoration process. We postulate that restored patterns in the deblurred image should resemble other sharp details in the input image. To this purpose, we establish the correspondence of regions that are similar up to Gaussian blur. When two regions are in correspondence, one can perform deblurring by using the sharpest of the two as a proposal. Our solution consists of two steps: First, estimate correspondence of similar patches and their relative amount of blurring; second, restore the input image by imposing the similarity of such recurring patterns as a prior. Our approach has been successfully tested on both real and synthetic data.
KeywordsInput Image Point Spread Function Image Restoration Deblurred Image Corneal Imaging
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