Hashing Image Patches for Zooming
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In this paper we present a Bayesian image zooming/super-resolution algorithm based on a patch based representation. We work on a patch based model with overlap and employ a Locally Linear Embedding (LLE) based approach as our data fidelity term in the Bayesian inference. The image prior imposes continuity constraints across the overlapping patches. We apply an error back-projection technique, with an approximate cross bilateral filter. The problem of nearest neighbor 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 hashing scheme and the robustness and inherent modularity and parallel structure achieved by the LLE setup. The ill-posedness of the image reconstruction problem is handled by the introduction of regularization priors which encode the knowledge present in vast collections of natural images. We present comparative results for both run-time as well as visual image quality based measurements.
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