Fast blind deconvolution using a deeper sparse patch-wise maximum gradient prior

Abstract

In this study, we propose a patch-wise maximum gradient (PMG) prior for effective blind image deblurring. Our work is motivated by the fact that the maximum gradient values of non-overlapping local patches are significantly diminished by blurring; we demonstrate this inherent property both theoretically and using real data. Based on this, we propose a blur kernel estimation model using an L0-regularized PMG prior and L0-regularized gradient prior. Compared with previous image priors, our PMG prior exhibits a stronger ability to distinguish between clear and blurred images. It also has a deeper sparseness, which significantly reduces the computational cost. To solve the proposed PMG and L0-regularized gradient terms, we design an efficient optimization algorithm by introducing a linear operator and improving the iteration strategy. Visual and quantitative experimental results show that our method can achieve excellent performance and is superior to state-of-the-art methods in terms of computational efficiency and recovery quality in various specific scenarios such as natural, face, saturated, and text images.