Blur Kernel Estimation Model with Combined Constraints for Blind Image Deblurring

Abstract

This paper proposes a blur kernel estimation model based on combined constraints involving both image and blur kernel constraints for blind image deblurring. We adopt L0 regularization term for constraining image gradient and dark channel of image gradient to protect image strong edges and suppress noise in image, and use L2 regularization term as hybrid constraints for blur kernel and its gradient to preserve blur kernel's sparsity and continuity respectively. In combined constraints, the constrained dark channel of image gradient, which is a dark channel prior, can also effectively help blind image deblurring in various scenarios, such as natural, face and text images. Moreover, we introduce a half-quadratic splitting optimization algorithm for solving the proposed model. We conduct extensive experiments and results demonstrate that the proposed method can better estimate blur kernel and achieve better visual quality of image deblurring on both synthetic and real-life blurred images.