A Blur-SURE-Based Approach to Kernel Estimation for Motion Deblurring

Abstract

Blind motion deblurring is a highly challenging inverse problem in image processing and low-level computer vision. In this paper, we propose a novel approach to identify the parameters (blur length and orientation) of motion blur from an observed image. The kernel estimation is based on a novel criterion — the minimization of a blurred Stein’s unbiased risk estimate (blur-SURE): an unbiased estimate of a filtered mean squared error. By incorporating a simple Wiener filtering into the blur-SURE, the motion blur is estimated by minimizing this new objective functional with high accuracy. We then perform non-blind deconvolution using the high-quality SURE-LET algorithm with the estimated kernel. The results of synthetic and real experiments are quite competitive with other state-of-the-art algorithms under a wide range of degradation scenarios both numerically and visually.