  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.
