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A Hierarchical Markov Random Field Model for Bayesian Blind Image Separation
2008
2008 Congress on Image and Signal Processing
In this paper we propose an hierarchical Markov random field (HMRF) model and the Bayesian estimation frame for separating noisy linear mixtures of images constituted by homogeneous patches. A latent Potts-Markov labeling field is introduced for each source image to enforce piecewise homogeneity of pixel values. Based on classification labels, the upper observable intensity field is modeled by the combination of Markovian smoothness of intensity inside a patch and conditional independence at
doi:10.1109/cisp.2008.6
fatcat:vmosopkqbbf53j6ixxktbxmc2i