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In this paper we present a new Bayesian methodology for the restoration of blurred and noisy images. Bayesian methods rely on image priors that encapsulate prior image knowledge and avoid the ill-posedness of the image restoration problems. Some of these priors depend on global variance parameters, unable to account for local characteristics. Here we first use variational methods to approximate probability posterior distributions for the global model to later use those distributions to define local and more realistic image models which lead to better restored images as it is shown in the experimental section.