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Single Image Super Resolution (SISR) methods aim to recover the clean imagesin high resolution from low resolution observations.A family of patch-basedapproaches have received considerable attention and development. The minimummean square error (MMSE) methodis a powerful image restoration method that usesa probability model on the patches of images. This paper proposes an algorithmto learn a jointgeneralized Gaussian mixture model (GGMM) from a pair of thelow resolution patches and the corresponding high resolution patches fromthereference data. We then reconstruct the high resolution image based on the MMSEmethod. Our numerical evaluations indicate that theMMSE-GGMM method competeswith other state of the art methods.

Patch-based image Super Resolution using generalized Gaussian mixture model