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In this paper we present a Bayesian image zooming/super-resolution algorithmbased on a patch based representation. We work on a patch based model withoverlap and employ a Locally Linear Embedding (LLE) based approach as our datafidelity term in the Bayesian inference. The image prior imposes continuityconstraints across the overlapping patches. We apply an error back-projectiontechnique, with an approximate cross bilateral filter. The problem of nearestneighbor search is handled by a variant of the locality sensitive hashing (LSH)scheme. The novelty of our work lies in the speed up achieved by the hashingscheme and the robustness and inherent modularity and parallel structureachieved by the LLE setup. The ill-posedness of the image reconstructionproblem is handled by the introduction of regularization priors which encodethe knowledge present in vast collections of natural images. We presentcomparative results for both run-time as well as visual image quality basedmeasurements.

Hashing Image Patches for Zooming