COMPARISON OF SIMULATED ANNEALING ALGORITHMS FOR IMAGE-RESTORATION

This paper presents a comparative study of four optimisation algorithm s based on simulated annealing: the Gibbs Sampler, the Metropolis algo rithm, the Iterated Conditional Modes, and an original method of rando m descent proposed by the authors. Comparison criteria that have been chosen are the convergence speed, the quality of the optimum obtained with a new specific energy function and the total computing time neces sary for image restoration. The four algorithms are applied to the spe cial case of restoration of images disturbed by a gaussian white noise . Moreover, a new function to be minimized and several efficient heuri stics allowing to decrease computing time are proposed. Some numerical experiments are given.