Robust and Adaptive Segmentation of Noisy Images Using Gibbs Random Field Models

A robust and adaptive algorithm for segmenting a noisy image is presented. The original (or noise-free) images are modeled by an underlying Gibbs random field (GRF) or equivalently by a Markov random field (MRF), and the driving noise is a mixture of an additive independent Gaussian noise and an outlier process. The processes of maximum a posteriori (MAP) segmentation and maximum-likelihood (ML) estimation for the image model parameters are carried out simultaneously. Both procedures, which are based on stochastic relaxation algorithms with a simulated annealing technique, are efficiently implementable on parallel architectures, such as artificial neural networks. Our proposed method is adaptive in that it recursively segments the noise-corrupted images and estimates the model parameters necessary for the segmentation procedure. We should emphasize that the proposed algorithm is robust in eliminating the effect caused by outliers. Finally, a number of computer simulation experiments are given to demonstrate the robustness and effectiveness of the recursive algorithm.