A Bayesian Nonparametric Approach to Image Super-resolution

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...

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Bibliographic Details
Published in:arXiv.org 2012-09
Main Authors: Gungor Polatkan, Zhou, Mingyuan, Lawrence, Carin, Blei, David, Daubechies, Ingrid
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Human performance
Image resolution
Quality assessment
Online Access:Get full text
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Summary: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.
ISSN:2331-8422