Pairwise Markov Model Applied to Unsupervised Image Separation

Selwa Rafi, Marc Castella, and Wojciech Pieczynski


Pairwise Markov Chain, Blind Source Separation, Image Separation, Show-Through Removal


The paper deals with blind separation and recovery of a noisy mixture of two binary signals on two sensors. Such a model can be applied in the context of recovery of scanned documents subject to show-through and bleed-through effects. The problem can be considered as a blind source separation one. Due to a complex noise and data structure, it is tackled from the more general approach of Bayesian restoration. The data is assumed to follow a Pairwise Markov Chain model: it generalizes Hidden Markov Chain models but it still allows one to calculate the a posteriori distributions of the data. The Expectation-Maximization (EM) and Iterative Conditional Estimation (ICE) methods are considered for parameter estimation, yielding an unsupervised processing. Finally, simulations show the interest of our approach on simulated and real data.

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