Istituto di Scienza e Tecnologie dell'Informazione     
Kayabol K., Kuruoglu E. E., Sankur B. Source separation in images via MRFs with variational approximation. In: European Signal Processing Conference (Poznan, Poland, 3-7 September 2007). Proceedings, vol. 1 European Signal Processing Association (EURASIP) (ed.). EURASIP, 2007.
The problem of source separation in two dimensions is studied in this paper. The problem is formulated in the Bayesian framework. The sources are modelled as MRFs to accommodate for the spatially correlated structure of the sources, which we exploit for separation in 2D. The difficulty of working analytically with general Gibbs distributions is overcome by using an approximate density. In this work, the Gibbs distribution is modelled by the product of directional Gaussians. The sources are estimated by Maximuma- Posteriori estimation using the approximate density as the prior. At each iteration of the MAP estimation, an annealing schedule is used for approximate density. This annealing schedule aids the algorithm to converge the global extremum. The mixing matrix is found by Maximum Likelihood estimation.
URL: http://www.eusipco2007.org/programme.html
Subject Source separation
Variational methods
Markov random fields
I.4.4 Restoration
G.3 Probability and Statistics. Markov processes
62M40 Random fields; image analysis
35A15 Variational methods
62M05 Markov processes: estimation

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