Bayesian methods for recovering a 2-D surface are discussed. It is assumed that there is a textural image that can be modeled by a Markov random field and that the original surface is composed of different surfaces, each of which is associated with one textural state. Both parametric and nonparametric methods are used to enforce smoothness of these surfaces. Iterative procedures are examined for simultaneous restoration of the textural image and estimation of underlying parameters. From the estimated textural image and the estimated parameters, an estimate for the original surface is obtained. Two illustrative examples are presented.
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