PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Kayabol K., Kuruoglu E. E., Sanz J. L., Sankur B., Salerno E., Herranz D. Adaptive langevin sampler for separation of t-distribution modelled astrophysical maps. Technical report, 2009.
 
 
Abstract
(English)
We propose to model the image differentials of astrophysical sources with Student's t-distribution and use them in the Bayesian source separation method as priors. We introduce an efficient Markov Chain Monte Carlo (MCMC) sampling scheme to unmix the astrophysical sources and describe the derivation details. In this scheme, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and reduces the computation time significantly (by two orders of magnitude). In addition, Student's t-distribution parameters are updated throughout the iterations. The results on astrophysical source separation are assessed with two performance criteria defined in the pixel and the frequency domains.
Subject Bayesian source separation
Student's t-distribution
Metropolis-Hastings
Langevin stochastic equation
Markov chain Monte Carlo
Multispectral denoising
I.4 Image Processing and Computer Vision
J.2 Physical Sciences and Engineering
62M40 Random fields; image analysis
65Cxx Probabilistic methods, simulation and stochastic differential equations


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