  This paper proposes a CS scheme that exploits the representational power of
restricted Boltzmann machines and deep learning architectures to model the
prior distribution of the sparsity pattern of signals belonging to the same
class. The determined probability distribution is then used in a maximum a
posteriori (MAP) approach for the reconstruction. The parameters of the prior
distribution are learned from training data. The motivation behind this
approach is to model the higher-order statistical dependencies between the
coefficients of the sparse representation, with the final goal of improving the
reconstruction. The performance of the proposed method is validated on the
Berkeley Segmentation Dataset and the MNIST Database of handwritten digits.
