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Image super-resolution with sparse representation prior on primitive patches

We focus on the problem of single image super-resolution in this paper. Given a low-resolution image, we seek to synthesize its underlying high-resolution details using a learning based method. Inspired by recent progress in compressive sensing, we use sparse representation prior to regularize this ill-posed problem. On the other hand, with natural image statistics taken into consideration, we enforce the prior only on those image patches associated with image primitives rather than on arbitrary ones. Specifically, each patch from primitive layer of the lowresolution image, which can be viewed as a low-dimensional projection of a high-resolution primitive patch, is conjectured to have a sparse representation concerning an over-complete dictionary. Under mild conditions, the sparse representation can be correctly restored from the low-dimensional projection according to the theory of compressive sensing. We also construct a dictionary using image primitive patches which works well on generic input images. Experiment results show the efficiency of our method by outperforming other learning-based methods both subjectively and objectively.