BAYESIAN RESTORATION USING A NEW HIERARCHICAL DIRECTIONAL CONTINUOUS EDGE IMAGE PRIOR By

In this paper we propose a class of image restoration algorithms based on the Bayesian approach and a new hierarchical spatially adaptive image prior. The proposed prior has two desirable features. First, it models the image discontinuities in different directions with a continuous value model. Thus, it generalizes the on/off (binary) line process idea used in previous image priors within the context of Markov Random Fields (MRF). Second, it is based on an acyclic graphical model, unlike MRFs, which greatly facilitates learning and inference. Using this new hierarchical prior two restoration algorithms are derived. The first is based on the maximum a posteriori (MAP) principle, and the second on the Bayesian methodology. Numerical experiments are presented that compare the proposed algorithms among themselves and with previous stationary and non stationary MRF based with line process algorithms. These experiments demonstrate the advantages of the proposed prior. The authors are with the Department of Computer Science, University of Ioannina, Ioannina, Greece 45110. E-mails of authors: {chanjohn,galatsanos,arly}@cs.uoi.gr, 2 Corresponding Author.