Source separation of astrophysical images modelled with Markov Random Field models

Astrophysical radiation maps provide images which are superpositions of various cosmological components such as the cosmic microwave background (CMB) radiation, galactic dust, synchrotron, free-free emission and extragalactic radio sources. All these components are of great interest to cosmologists and in particular CMB, in addition to being the picture of the early universe, carries important information that would help us to choose between existing evolution theories of the universe. In this work we present a technique for the separation of these components in the presence of receiver noise. In contrast with most work in the literature, we make use of the spatial information in the images in the form of correlation between pixels which we model using Markov Random Fields. The spatial information is included in the MRF model through a Bayesian estimation framework. We provide comparisons with the results obtained by FastICA.