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Istituto di Scienza e Tecnologie dell'Informazione     
Kayabol K., Kuruoglu E. E. Non-stationary t-distribution prior for image source separation from blurred observations. In: LVA/ICA 2010 - Latent Variable Analysis and Signal Separation. 9th International Conference (St. Malo, France, 27-30 September 2010). Proceedings, pp. 506 - 513. Vincent Vigneron, Vicente Zarzoso, Eric Moreau, Rémi Gribonval, Emmanuel Vincent (eds.). (Lecture Notes in Computer Science, vol. 6365). Springer, 2010.
 
 
Abstract
(English)
We propose a non-stationary spatial image model for blind image separation problem. Our model is defined on first order image differentials. We model the image differentials using t-distribution with space varying scale parameters. This prior image model has been used in the Bayesian formulation and the image source are estimated using a Langevin sampler method. We have tested the proposed model on astrophysical image mixtures and obtained better results regarding to stationary model.
URL: http://www.springerlink.com/content/r85885j6t37n3642/
DOI: 10.1007/978-3-642-15995-4_63
Subject Markov random fields
Student-t distribution
Image separation
Astrophysical images
G.3 Probability and Statistics. Markov processes
G.3 Probability and Statistics. Probabilistic algorithms (including Monte Carlo)
J.2 Physical Sciences and Engineering. Astronomy


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