Half quadratic splitting method combined with convolution neural network for blind image deblurring

Blind image deblurring is the process of recovering the original image from a degraded image under unknown point spread function, and it is the solution to an ill-posed inverse problem. In this paper, the blurry image is firstly divided into skeleton image and blur kernel, aiming to achieve accurate blur kernel estimation. Then the advantages of model-based optimization method and discriminative learning method are integrated through variable splitting technique. Finally, a trained convolutional neural network (CNN) is used as a module to be inserted into a model-based optimization method to solve the problem of blind image deblurring more effectively. By comparing visual and quantitative experimental data, the network proposed in this paper can provide powerful prior information for blind image deblurring and the restoration effects can approximate or exceed those of some representative algorithms.