Bayesian region selection for adaptive dictionary-based Super-Resolution

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Document typeConference lecture
Defense date2013
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Abstract
The performance of dictionary-based super-resolution (SR) strongly depends on the
contents of the training dataset. Nevertheless, many dictionary-based SR methods randomly select patches from of a larger set of training images to build their dictionaries
[
8
,
14
,
19
,
20
], thus relying on patches being diverse enough. This paper describes
a dictionary building method for SR based on adaptively selecting an optimal subset of
patches out of the training images. Each training image is divided into sub-image entities,
named regions, of such a size that texture consistency is preserved and high-frequency
(HF) energy is present. For each input patch to super-resolve, the best-fitting region is
found through a Bayesian selection. In order to handle the high number of regions in
the training dataset, a local Naive Bayes Nearest Neighbor (NBNN) approach is used.
Trained with this adapted subset of patches, sparse coding SR is applied to recover the
high-resolution image. Experimental results demonstrate that using our adaptive algo-
rithm produces an improvement in SR performance with respect to non-adaptive training.
CitationPérez-Pellitero, E. [et al.]. Bayesian region selection for adaptive dictionary-based Super-Resolution. A: British Machine Vision Conference. "BMVC 2013: Proceedings of the 13th British Machine Vision Conference: 9-13 September 2013, Bristol University, UK". Bristol: 2013, p. 1-11.
ISBN1-901-725-46-4
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