Joint Bayesian separation and restoration of CMB 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. 
K. Kayabol, J.L. Sanz, D. Herranz, E.E. Kuruoglu and E. Salerno
astro-ph.IM astro-ph.CO