Restoration of severely blurred high range images using stochastic and deterministic relaxation algorithms in compound Gauss-Markov random fields

Over the last few years, a growing number of researchers from varied discip lines have been utilizing Markov random fields (MRF) models for developing optimal, robust algorithms for Various problems, such as texture analysis, image synthesis, classification and segmentation. surface reconstruction, i ntegration of several low level vision modules, sensor fusion and image res toration. However, no much work has been reported on the use of Simulated A nnealing (SA) and Iterative Conditional Mode (ICM) algorithms for maximum a posteriori estimation in image restoration problems when severe blurring i s present. In this paper we examine the use of compound Gauss-Markov random fields (CGMRF) to restore severely blurred high range images. For this deb lurring problem, the convergence of the SA and ICM algorithms has not been established. We propose two new iterative restoration algorithms which can be considered as extensions of the classical SA and ICM approaches and whos e convergence is established. Finally, they are tested on real and syntheti c images and the results compared with the restorations obtained by other i terative schemes. (C) 2000 Pattern Recognition Society. Published by Elsevi er Science Ltd. All rights reserved.