Pairwise and Hidden Markov Random Fields in Image Segmentation

The purpose of this paper is to identify the similarities and differences between two image restoration approaches based on Markov field modeling. The first one is the well-known Bayesian approach which models the unknowns with a Markovian prior. In the second approach, as proposed by Pieczynski and Tebbache [1], the pair unknowns–observations as a whole is considered Markovian. The two approaches are compared based on their posterior distribution, synthetic results and real examples, when applied to the segmentation of degraded images.