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<p>In an image restoration problem one usually has two different kinds of information. In the first stage, one has knowledge about the structural form of the noise and local characteristics of the restoration. These noise and image models normally depend on unknown hyperparameters. The hierarchical Bayesian approach adds a second stage by putting a hyperprior on the hyperparameters, where information about those hyperparameters is included. In this work the author applies the hierarchical Bayesian approach to image restoration problems and compares it with other approaches in handling the estimation of the hyperparameters.</p>
image restoration; Bayes methods; maximum likelihood estimation; astronomy; hierarchical Bayesian approach; image restoration; astronomical images; noise; local characteristics; image models; hyperprior; hyperparameters estimation

R. Molina, "On the Hierarchical Bayesian Approach to Image Restoration: Applications to Astronomical Images," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 1122-1128, 1994.
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