Introduction - If you have any usage issues, please Google them yourself
The image deblurring problem is a typical inverse problem. Subject to the inherent constraints of the inverse problem, it is very difficult to find the true solution or the visual habit in its large solution space. Moreover, the noise introduced in the observation process restricts the quality of the solution. In view of the inverse problem of image deblurring, from the model point of view, the existing methods focus mainly on two major directions: 1) seek a more appropriate image prior knowledge to construct a more precise prior model. These priors have evolved from many properties, such as smoothness, piecewise smoothness, gradient sparsity, etc., and have been widely used in blind and non blind restoration methods. 2) based on the analysis of the observed noise, a more reasonable fidelity term is designed.