A bayesian nonparametric approach to image super-resolution

Gungor Polatkan, Mingyuan Zhou, Lawrence Carin, David Blei, Ingrid Daubechies

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

Super-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.
Original languageEnglish (US)
Pages (from-to)346-358
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume37
Issue number2
DOIs
StatePublished - Feb 1 2015
Externally publishedYes

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