Gradient Cepstrum Combined with Simplified Extreme Channel Prior for Blind Deconvolution.

As a well-known ill-conditional problem in the image processing field, image deblurring has become a hot topic recently. The prior-based blind image deblurring methods have recently shown promising effectiveness. A lot of advanced algorithms such as dark channel prior, bright channel prior, and local maximum gradient prior are time-consuming since nonlinear operators are involved. Presented in this paper is a fast blind image deblurring algorithm which uses the simplified extreme channel prior (SECP) and gradient cepstrum. The inspiration for this work comes from the fact that the simplified bright channel prior (SBCP) of the clear image has fewer non-one elements than the blurred one. We propose a novel SECP based on the proposed SBCP and the simplified dark channel prior (SDCP). By enforcing the L 0 norm constraint to the terms involving SECP and incorporating them into the traditional deblurring framework, an effective optimization scheme is explored. Furthermore, gradient cepstrum is used to determine the size of the initial kernel and restrain excessive iterations in each scale. Experimental results illustrate that our algorithm outperforms the state-of-the-art deblurring algorithms in terms of computational efficiency and deblurring effect on both benchmark datasets and real-world blur scenes. [ABSTRACT FROM AUTHOR]