Stochastic relaxation for MAP restoration of gray level images with multiplicative noise

This paper is concerned with developing alagorithms for maximum a posteriori (MAP) restoration of gray level images degraded by multiplicative noise. The MAP algorithm requires the probability density function of the original undegraded image which is rarely available and the probability density function of the corrupting noise. By assuming that the original image is represented by a 2-D noncausal Gaussian Markov random field (GMRF) model, the MAP algorithm is written in terms of GMRF model parameters. The computer implementation of the MAP estimator equations is realized by a stochastic relaxation (SR) algorithm. The SR algorithm generates a sequence of images which converges in probability to the global MAP estimate. Several examples of restoration of the gray level image degraded by multiplicative noise are included.