Details
| Originalsprache | Englisch |
|---|---|
| Publikationsstatus | Veröffentlicht - 2013 |
| Veranstaltung | 2013 24th British Machine Vision Conference, BMVC 2013 - Bristol, Großbritannien / Vereinigtes Königreich Dauer: 9 Sept. 2013 → 13 Sept. 2013 |
Konferenz
| Konferenz | 2013 24th British Machine Vision Conference, BMVC 2013 |
|---|---|
| Land/Gebiet | Großbritannien / Vereinigtes Königreich |
| Ort | Bristol |
| Zeitraum | 9 Sept. 2013 → 13 Sept. 2013 |
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 algorithm produces an improvement in SR performance with respect to non-adaptive training.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
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2013. Beitrag in 2013 24th British Machine Vision Conference, BMVC 2013, Bristol, Großbritannien / Vereinigtes Königreich.
Publikation: Konferenzbeitrag › Paper › Forschung › Peer-Review
}
TY - CONF
T1 - Bayesian region selection for adaptive dictionary-based super-resolution
AU - Pérez-Pellitero, Eduardo
AU - Salvador, Jordi
AU - Ruiz-Hidalgo, Javier
AU - Rosenhahn, Bodo
PY - 2013
Y1 - 2013
N2 - 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 algorithm produces an improvement in SR performance with respect to non-adaptive training.
AB - 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 algorithm produces an improvement in SR performance with respect to non-adaptive training.
UR - http://www.scopus.com/inward/record.url?scp=84898450539&partnerID=8YFLogxK
U2 - 10.5244/C.27.37
DO - 10.5244/C.27.37
M3 - Paper
AN - SCOPUS:84898450539
T2 - 2013 24th British Machine Vision Conference, BMVC 2013
Y2 - 9 September 2013 through 13 September 2013
ER -