Smoothing priors for blind image deblurring

Blind image deblurring is one of the most critical issues in digital image processing. The main goal of blind image deblurring is to estimate the blur kernel and the intermediate image with a blurry image as input. In this paper, we propose a new algorithm for blind image deblurring based on the image smoothing priors. We notice that the salient edge is significant in estimating the blur kernel. If we can find a strategy that can preserve the salient edges of images and smooth out unnecessary details in the deblurring process, the estimated blur kernel will be more accurate. According to this observation, we draw on the experience of image smoothing and propose a new model based on the smoothing priors. For binary images, we extend our model by considering binary constraints. We also extend our method to the nonuniform deblurring problem. Numerically, we use the half-quadratic splitting method to minimize the optimization problem. We also propose a new template-based interpolated algorithm to solve the Lp minimization problem. In the experiment part, we test our method on various datasets to show its effectiveness. Compared with other related methods, our method can estimate blur kernels more accurately and generate fewer artifacts.