Relaxation algorithms for MAP estimation of gray-level images with multiplicative noise

The authors present a comparison between stochastic and deterministic relaxation algorithms for maximum a posteriori estimation of gray-level images modeled by noncausal Gauss-Markov random fields (GMRF) and corrupted by film grain noise. The degradation involves nonlinear transformation and multiplicative noise. Parameters for the GMRF model were estimated from the original image using maximum-likelihood techniques. To overcome modeling errors, a constraint minimization approach is suggested for estimating the parameters to ensure the positivity of the power spectral density function. Real image experiments with various noise variances and magnitudes of the nonlinear transformation are presented