Compound Gauss Markov random field model for image segmentation and restoration

A compound Gauss-Markov random field (CGMRF) that models nonstationarity in images is proposed for the segmentation and restoration of blurred and noisy images. At the top level of the CGMRF, a label process, which segments the image into K regions, is modeled by a Gibbs random field (GRF). At the bottom level, pixel intensities in each region are modeled by a stationary, noncausal GMRF. maximum a posteriori (MAP) estimates of segmented and restored images are obtained by maximizing their joint a posteriori distribution, using model parameters identified from the noisy image. A stochastic relaxation method is used for optimization. For faster convergence, deterministic relaxation is implemented. Experimental results on segmenting and restoring a noisy image are presented