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. In: Ieee Transactions on Image Processing, vol. 19 (9) pp. 2357 - 2368. IEEE, 2010.
 
 
Abstract
(English)
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.
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5451169
DOI: 10.1109/TIP.2010.2048613
Subject Bayesian source separation
Langevin stochastic equation
Student's t-distribution
Markov chain Monte Carlo
Metropolis-Hastings
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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