Blind Image Deblurring With Joint Extreme Channels And L0-Regularized Intensity And Gradient Priors

Abstract

The extreme channels prior (ECP) relies on the bright and dark channels of an image, and the corresponding ECP-based methods perform well in blind image deblurring. However, we experimentally observe that the pixel values of dark and bright channels in some images are not concentratedly distributed on 0 and 1 respectively. Based on this observation, we develop a model with a joint prior which combines the extreme channels prior and the $L_{0}-$regularized intensity and gradient prior for blind image deblurring, and previous image deblurring approaches based on dark channel prior, $L_{0^{-}}$ regularized intensity and gradient, and extreme channels prior can be seen as a particular case of our model. Then we derive an efficient optimization algorithm using the half-quadratic splitting method to address the non-convex $L_{0}-$minimization problem. A large number of experiments are finally performed to demonstrate the superiority of the proposed model in details restoration and artifacts removal, and our model outperforms several leading deblurring approaches in terms of subjective results and objective assessments. In addition, our method is more applicable for deblurring natural, text and face images which do not contain many bright or dark pixels.