  Image super-resolution (SR) is one of the long-standing and active topics in
image processing community. A large body of works for image super resolution
formulate the problem with Bayesian modeling techniques and then obtain its
Maximum-A-Posteriori (MAP) solution, which actually boils down to a regularized
regression task over separable regularization term. Although straightforward,
this approach cannot exploit the full potential offered by the probabilistic
modeling, as only the posterior mode is sought. Also, the separable property of
the regularization term can not capture any correlations between the sparse
coefficients, which sacrifices much on its modeling accuracy. We propose a
Bayesian image SR algorithm via sparse modeling of natural images. The sparsity
property of the latent high resolution image is exploited by introducing latent
variables into the high-order Markov Random Field (MRF) which capture the
content adaptive variance by pixel-wise adaptation. The high-resolution image
is estimated via Empirical Bayesian estimation scheme, which is substantially
faster than our previous approach based on Markov Chain Monte Carlo sampling
[1]. It is shown that the actual cost function for the proposed approach
actually incorporates a non-factorial regularization term over the sparse
coefficients. Experimental results indicate that the proposed method can
generate competitive or better results than \emph{state-of-the-art} SR
algorithms.
