The basic blur distribution approach considered in the restoration of degraded images uses a Gaussian structure to cover a large class of possible distributions. In contrast to this general hypothesis, a common problem encountered in restoration is that the restoration algorithm, especially working in real time, might have large errors with unexpected results if the distribution of image blur based on different media such as atmosphere, x rays, etc. is not known. From this point of view, information about the distribution of blur operating on image is an essential step toward a better restoration to obtain a high-quality image from the degraded one. We introduce a novel approach for restoration of images by using edge and distribution information of the degraded image. For that purpose, first, the distribution model of the degradation process is estimated by a correlation between actual image and reference distribution test models. Then, the proposed algorithm estimates restoration filter model coefficients according to the estimated distribution model and edge information of the degraded image. Simulation results of image restoration illustrate the performance of the proposed method.