Image blind restoration using priors of sharp images

The imaging systems are affected by various interferences, causing image blur and noise. In order to solve the problem, a new blind restoration method was proposed to recover the images using knowledge of sharp images. Firstly, the statistics of gradient magnitude values of sharp images were analyzed, and the images were divided into two parts, the highly textured region and the flat region. Fitting the probability distribution functions, the priors of sharp images were obtained, and the constraint was injected which the probability distribution of the flat region was little affected by blur kernel, aiming to avoid the image ringing. Then, the image noise model was established and divided into two parts, the Gauss noise and random noise, with propose of keeping off outliers caused by the over-saturated pixels. Finally, the maximum posterior probability was utilized to construct cost function, and emploied the expectation-maximization algorithm and iterative shrinkage algorithm were emploied to solve the cost function to recover image. The experiments show that it archive in recovering much image details, sharpening edges and avoid artifacts.