SIMULATED ANNEALING IN COMPOUND GAUSSIAN RANDOM-FIELDS

It is well-known that searching for globally optimal solutions is very difficult. In most cases only locally optimal solutions are found, usually by deterministic searches. Recently, a stochastic relaxation technique called simulated annealing has been developed to search for a globally optimal solution in image estimation and restoration problems. The convergence of simulated annealing has been'proved only for random fields with a compact range space. Because of this, images were modeled as random fields with bounded discrete or continuous values. However, in most image processing problems, it is more natural to model the image as a random field with values in a noncompact space, e.g., conditional Gaussian models. The proof of convergence of the stochastic relaxation method is extended to a class of compound Gauss-Markov random fields. Simulation results are provided to show the power of these methods. © 1990 IEEE