Articolo in rivista, 2010, ENG, 10.1109/TIP.2010.2048613
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
Markov Random fields, Image analysis, Bayesian source separation, T-distribution, Langevin stochastic equation, Student's t-distribution
Kayabol Koray, Kuruoglu Ercan Engin, Salerno Emanuele
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
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