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In this paper we propose a new iterative Bayesian non stationary image restoration algorithm. The main novelty of this approach is the introduction of a hierarchical non stationary image prior. Based on this prior and the generative graphical model for the observations, Bayesian inference is performed integrating out the hidden variables. An interesting byproduct of this approach is the justification, using a Bayesian framework, of previous non stationary image restoration formulations thatdoi:10.1109/icpr.2004.1333866 dblp:conf/icpr/ChantasGL04 fatcat:lx45aaiupngknlmfpuzccmkdo4