Blind motion image deblurring using an effective blur kernel prior

Abstract

Blind image deblurring, i.e., reconstructing a sharp version of a blurred image, is generally an ill-posed problem, as both the blur kernel and the sharp image are unknown. To solve such problem, one must use effective image and blur kernel priors. In this paper, a blind image deblurring method is proposed, which uses an effective image prior based on both the first and second order gradients of the image. This prior causes to properly reconstruct salient edges which provide reliable edge information for kernel estimation in the intermediate latent image. This prior along with a hyper-Laplacian kernel prior can be used to solve the optimization problem in the form of maximum-a posteriori-problem, and hence obtaine the blur kernel with a high accuracy. The efficiency of the proposed method is demonstrated by performing several quantitative and qualitative comparisons with the state-of-the-art methods, on both a benchmark image dataset and real-world motion blurred images.