Adaptive langevin sampler for separation of t-distribution modelled astrophysical maps

Authors:
Koray Kayabol;Ercan E. Kuruoğlu;José Luis Sanz;Bülent Sankur;Emanuele Salerno;Diego Herranz
Affiliations:
Institute of Information Science and Technologies, Italian National Research Council, Pisa, Italy;Institute of Information Science and Technologies, Italian National Research Council, Pisa, Italy;Institute of Physics of Cantbabria, University of Cantabria, Santander, Spain;Electrical and Electronics Engineering Department, Bogazici University, Bebek, Istanbul, Turkey;Institute of Information Science and Technologies, Italian National Research Council, Pisa, Italy;Institute of Physics of Cantbabria, University of Cantabria, Santander, Spain
Venue:
IEEE Transactions on Image Processing
Year:
2010

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Abstract

We propose to model the image differentials of astrophysical source maps by Student's t-distribution and to 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.