Bayesian Image Modeling by Means of a Generalized Sparse Prior and Loopy Belief Propagation
Kazuyuki Tanaka, Muneki Yasuda, D. Michael Titterington · Journal of the Physical Society of Japan · 2012
Bayesian image modeling is presented based on a generalized sparse prior probability distribution. Our prior includes sparsity in each interaction term between every pair of neighbouring pixels in Markov random fields. A new scheme for hyperparameter estimation is based on the conditional maximization of entropy in our generalized sparse prior. In addition, the criterion used for defining the optimal value for sparseness in interactions is that of the maximization of marginal likelihood. Our practical algorithm is based on loopy belief propagation.