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Jianchao Yang, John Wright, Thomas Huang, Yi MaCVPR 2008Image Super-Resolution as Sparse Representation of Raw Image PatchesOutlineIntroductionSuper-resolution from SparsityLocal Model from Sparse RepresentationEnforcing Global Reconstruction ConstraintGlobal Optimization InterpretationDictionary PreparationExperimentsDiscussion2IntroductionTo generate a super-resolution (SR) image requires multiple low-resolution images of the same scene, typically aligned with sub-pixel accuracyMAP (maximum a-posteriori)Markov Random Field (MRF) solved by belief propagation Bilateral Total VariationThe SR task is cast as the inverse problem of recovering the original high-resolution image by fusing the low-resolution images3IntroductionLet be an overcomplete dictionary of K prototype signal-atomsSuppose a signal can be represented as a sparse linear combination of these atomsThe signal vector can be written as , whereis a vector with very few (