Deterministic parallel computation of Bayesian deblurring using cluster approximations

A family of approximations to Bayesian estimators based on Markov random fields models of images and mean squared error reconstruction criteria is described. The computation of the estimator requires the solution of a multivariable fixed point problem for which existence, uniqueness, and convergent algorithm results are stated. These algorithms preserve the structure of the grey levels. Two simple examples are given which show excellent performance.<
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