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Image priors based on products have been recognized to offer many advantages since they provide the ability to enforce simultaneously multiple constraints. However, they are inconvenient for Bayesian inference since their normalization constant cannot be found in closed form. In this paper a new Bayesian framework is proposed for the image restoration(More)
In this paper, we propose a maximum a posteriori framework for the super-resolution problem, i.e., reconstructing high-resolution images from shifted, rotated, low-resolution degraded observations. The main contributions of this work are two; first, the use of a new locally adaptive edge preserving prior for the super-resolution problem. Second an efficient(More)
Image priors based on products have been recognized to offer many advantages because they allow simultaneous enforcement of multiple constraints. However, they are inconvenient for Bayesian inference because it is hard to find their normalization constant in closed form. In this paper, a new Bayesian algorithm is proposed for the image restoration problem(More)
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