Bayesian image blind restoration based on differential evolution optimization

The difficulty of image blind restoration is the lack of sufficient information of the point spread function to cause the ill-posed problem. In order to achieve satisfactory results of image restoration, and at the same time to speed up the restoration, a Bayesian image blind restoration method based on differential evolution optimization is proposed. Firstly, the Gauss model and Laplace model are introduced as the priori model of the original image and the point spread function. Secondly, the unknown parameters are described by Jeffrey prior distribution. Finally, the differential evolution optimization method is used to alternately estimate the original image; the point spread function and the optimal value of the parameter through iteration. Experimental results prove the effectiveness of the proposed method, and it can get better restoration results compared with other similar methods.