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.
