  Single Image Super Resolution (SISR) methods aim to recover the clean images
in high resolution from low resolution observations.A family of patch-based
approaches have received considerable attention and development. The minimum
mean square error (MMSE) methodis a powerful image restoration method that uses
a probability model on the patches of images. This paper proposes an algorithm
to learn a jointgeneralized Gaussian mixture model (GGMM) from a pair of the
low resolution patches and the corresponding high resolution patches fromthe
reference data. We then reconstruct the high resolution image based on the MMSE
method. Our numerical evaluations indicate that theMMSE-GGMM method competes
with other state of the art methods.
