Bayesian region selection for adaptive dictionary-based Super-Resolution
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/21666
Tipus de documentComunicació de congrés
Data publicació2013
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
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.
CitacióPérez-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
Fitxers | Descripció | Mida | Format | Visualitza |
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bmvc_review.pdf | Article principal | 2,592Mb | Visualitza/Obre |