Image estimation by stochastic relaxation in the compound Gaussian case

Concerns developing algorithms for obtaining the maximum a posteriori probability (MAP) estimate from blurred and noisy images modeled as compound Gauss-Markov random fields. These models consist of several image submodels having different characteristics along with a structure model, a 2D Markov chain, which governs transitions between these image submodels. Compound random field models are attractive for image estimation because the resulting estimates do not suffer the over-smoothing of edges that occurs when one employs linear shift-invariant (LSI) models.<>