  this paper we consider the problem of separating noisy instantaneous linear
mixtures of document images in the Bayesian framework. The source image is
modeled hierarchically by a latent labeling process representing the common
classifications of document objects among different color channels and the
intensity process of pixels given the class labels. A Potts Markov random field
is used to model regional regularity of the classification labels inside object
regions. Local dependency between neighboring pixels can also be accounted by
smoothness constraint on their intensities. Within the Bayesian approach, all
unknowns including the source, the classification, the mixing coefficients and
the distribution parameters of these variables are estimated from their
posterior laws. The corresponding Bayesian computations are done by MCMC
sampling algorithm. Results from experiments on synthetic and real image
mixtures are presented to illustrate the performance of the proposed method.
