PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Kayabol K., Salerno E., Sanz J. L., Herranz D., Kuruoglu E. E. Blind source separation from multi-channel observations with channel-variant spatial resolutions. In: EUSIPCO 2010 - European Signal Processing Conference (Aalborg, Denmark, 23 - 27 August 2010). Proceedings, pp. 1077 - 1081. EURASIP, 2010.
 
 
Abstract
(English)
We propose a Bayesian method for separation and reconstruction of multiple source images from multi-channel observations with different resolutions and sizes. We reconstruct the sources by exploiting each observation channel at its exact resolution and size. The source maps are estimated by sampling the posteriors through a Monte Carlo scheme driven by an adaptive Langevin sampler. We use the t-distribution as prior image model. All the parameters of the posterior distribution are estimated iteratively along the algorithm. We experimented the proposed technique with the simulated astrophysical observations. These data are normally characterized by their channel-variant spatial resolution. Unlike most of the spatial-domain separation methods proposed so far, our strategy allows us to exploit each channel map at its exact resolution and size.
Subject Bayesian source separation
Multichannel Deconvolution
Channel variant spatial resolution
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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