Non-stationary t-distribution prior for image source separation from blurred observations

Kayabol K, Kuruoglu E E

Physical Sciences and Engineering. Astronomy  Probability and Statistics. Markov processes  Student-t distribution  Probability and Statistics. Probabilistic algorithms (including Monte Carlo)  Markov random fields 

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.

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