Joint Bayesian separation and restoration of cosmic microwave background from convolutional mixtures
Joint Bayesian separation and restoration of cosmic microwave background from convolutional mixtures
We propose a Bayesian approach to joint source separation and restoration for astrophysical diffuse sources. We constitute a prior statistical model for the source images by using their gradient maps. We assume a t-distribution for the gradient maps in different directions, because it is able to fit both smooth and sparse data. A Monte Carlo technique, called Langevin sampler, is used to estimate the source images and all the model parameters are estimated by using deterministic techniques.
KK undertook this work with the support of the ICTP Programme for Training and Research in Italian Laboratories, Trieste, Italy, through a specific operational agreement with CNR-ISTI, Italy. Partial support has also been given by the Italian Space Agency (ASI), under the project COFIS (Cosmology and Fundamental Physics). The project is partially supported by CNR-CSIC bilateral project no. 2008IT0059.
Peer Reviewed
Astrophysical component separation; J.2 Physical sciences and engineering. Astronomy, Diffuse radiation, Techniques: image processing, Astrophysical component separation; J.2 Ph, Cosmic background radiation, Methods: statistical
Astrophysical component separation; J.2 Physical sciences and engineering. Astronomy, Diffuse radiation, Techniques: image processing, Astrophysical component separation; J.2 Ph, Cosmic background radiation, Methods: statistical
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