Blind deblurring with patch-wise second-order gradient prior

Abstract

The patch-based image priors have been successfully applied to blind image deblurring algorithms. But those priors are time-consuming since too many non-linear operators are involved. However, the solving of these prior needs to use nonlinear operators that greatly decrease the computational efficiency. Proposed in this study is a simply patch-wise image prior that uses non-overlapped local patches to compute the local maximal second-order gradient of an image. We find that the values of the patch-wise second-order gradient (PSG) of an image decrease with the motion blur process. A new optimization algorithm is proposed by combining L1 regularized PSG with the maximum posterior probability. Besides, kernel similarity constraint is employed to control the iteration times to reduce the computational costs. Comparative experiments on mainstream datasets show that the results of the presented algorithm are generally better than those of other algorithms on both quantitative contrast and visual contrast.