Weighted Median Predictive Techniques for Coe cient Estimation in NonGaussian Markov Random Fields

NonGaussian Markov image models are e ective in the preservation of edge detail in Bayesian formulations of restoration and reconstruction problems. Included in these models are coe cients quantifying the statistical links among pixels in local cliques, which are typically assumed to have an inverse dependence on distance among the corresponding neighboring pixels. Estimation of these coe cients is a nontrivial task for NonGaussian models. We present results for coe cient estimation for a model which is particularly e ective for edge preservation and noise suppression, using a predictive technique analogous to estimation of the weights of optimal weighted median lters.