Adaptive Shrinkage Cascades for Blind Image Deconvolution

Recently emerged discriminative non-blind deconvolution methods achieve excellent performance with only a fraction of computation cost w.r.t. generative competitors, but their extension to blind deconvolution field was seldom addressed in a practical manner, albeit equally vital in image restoration area. We propose a novel framework for effective blind image deblurring by patch-wise prior based adaptive shrinkage cascades, which introduces the powerful internal patch-based image statistics to the non-blind shrinkage field formulations. The rich expressiveness of internal patch prior brings instance-specific adaptivity to alternating kernel refinement between neighboring shrinkage cascades, while shrinkage model trained from varieties of natural image collections benefits internal patch-wise prior inference with external information and superior efficiency.