Dual-Channel Contrast Prior for Blind Image Deblurring

In this article, a dual-channel contrast prior (Dual-CP) is proposed for blind image deblurring. The prior is motivated by the observation that image contrast will significantly degenerate after the blurring process, which is proved in both mathematically and empirically. Based on this inherent property of the blurring process, we analyze the variation of contrast influenced by blur and research the feasibility for using contrast prior to estimate blur kernel. We model the contrast by the difference between the dark channel and the bright channel. By maximizing the contrast in the local patch, we can obtain a reliable result which contains sharp edges and is beneficial for kernel estimation. To solve this non-convex nonlinear problem, we develop an efficient optimization method with the auxiliary variable idea and alternate direction minimization. Extensive experiments on real and synthetic blurry sets demonstrate that the proposed algorithm has good performance and exhibits competitiveness compared with state-of-the-art methods. Besides, we show that the proposed method can be applied to non-uniform deblurring.