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A Bayesian Nonparametric Approach to Image Super-Resolution.

Author(s): Polatkan, Gungor; Zhou, Mingyuan; Carin, Lawrence; Blei, David; Daubechies, Ingrid

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dc.contributor.authorPolatkan, Gungor-
dc.contributor.authorZhou, Mingyuan-
dc.contributor.authorCarin, Lawrence-
dc.contributor.authorBlei, David-
dc.contributor.authorDaubechies, Ingrid-
dc.date.accessioned2020-04-01T13:21:30Z-
dc.date.available2020-04-01T13:21:30Z-
dc.date.issued2015-02-01en_US
dc.identifier.citationPolatkan, Gungor, Zhou, Mingyuan, Carin, Lawrence, Blei, David, Daubechies, Ingrid. (2015). A Bayesian Nonparametric Approach to Image Super-Resolution.. IEEE transactions on pattern analysis and machine intelligence, 37 (2), 346 - 358. doi:10.1109/tpami.2014.2321404en_US
dc.identifier.issn0162-8828-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1jf6n-
dc.description.abstractSuper-resolution methods form high-resolution images from low-resolution images. In this paper, we develop a new Bayesian nonparametric model for super-resolution. Our method uses a beta-Bernoulli process to learn a set of recurring visual patterns, called dictionary elements, from the data. Because it is nonparametric, the number of elements found is also determined from the data. We test the results on both benchmark and natural images, comparing with several other models from the research literature. We perform large-scale human evaluation experiments to assess the visual quality of the results. In a first implementation, we use Gibbs sampling to approximate the posterior. However, this algorithm is not feasible for large-scale data. To circumvent this, we then develop an online variational Bayes (VB) algorithm. This algorithm finds high quality dictionaries in a fraction of the time needed by the Gibbs sampler.en_US
dc.format.extent346 - 358en_US
dc.languageengen_US
dc.language.isoen_USen_US
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
dc.rightsAuthor's manuscripten_US
dc.titleA Bayesian Nonparametric Approach to Image Super-Resolution.en_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1109/tpami.2014.2321404-
dc.identifier.eissn1939-3539-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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