Articolo in rivista, 2010, ENG, 10.1109/TIP.2010.2048613

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

Kayabol K.; Kuruoglu E. E.; Sanz J. L.; Sankur B.; Salerno E.; Herranz D.

CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; IFCA, Cantabria, Spagna; Bogazici University, Turchia; CNR-ISTI, Pisa, Italy; IFCA, Cantabria, Spagna

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.

IEEE transactions on image processing 19 (9), pp. 2357–2368

Keywords

Markov Random fields, Image analysis, Bayesian source separation, T-distribution, Langevin stochastic equation, Student's t-distribution

CNR authors

Kayabol Koray, Kuruoglu Ercan Engin, Salerno Emanuele

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 44401

Year: 2010

Type: Articolo in rivista

Last update: 2018-01-22 13:29:14.000

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:44401

DOI: 10.1109/TIP.2010.2048613

ISI Web of Science (WOS): 000283124600010

Scopus: 2-s2.0-77955807413