Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | ACCV 2014 |
| Untertitel | Computer Vision -- ACCV 2014 |
| Seiten | 346-359 |
| Seitenumfang | 14 |
| Band | 9005 |
| Publikationsstatus | Veröffentlicht - 16 Apr. 2015 |
| Veranstaltung | 12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapur Dauer: 1 Nov. 2014 → 5 Nov. 2014 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Herausgeber (Verlag) | Springer Verlag |
| ISSN (Print) | 0302-9743 |
Abstract
Regression-based Super-Resolution (SR) addresses the upscaling problem by learning a mapping function (i.e. regressor) from the low-resolution to the high-resolution manifold. Under the locally linear assumption, this complex non-linear mapping can be properly modeled by a set of linear regressors distributed across the manifold. In such methods, most of the testing time is spent searching for the right regressor within this trained set. In this paper we propose a novel inverse-search approach for regression-based SR. Instead of performing a search from the image to the dictionary of regressors, the search is done inversely from the regressors’ dictionary to the image patches. We approximate this framework by applying spherical hashing to both image and regressors, which reduces the inverse search into computing a trained function. Additionally, we propose an improved training scheme for SR linear regressors which improves perceived and objective quality. By merging both contributions we improve speed and quality compared to the stateof- the-art.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
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ACCV 2014: Computer Vision -- ACCV 2014. Band 9005 2015. S. 346-359 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Fast super-resolution via dense local training and inverse regressor search
AU - Pérez-Pellitero, Eduardo
AU - Salvador, Jordi
AU - Torres-Xirau, Iban
AU - Ruiz-Hidalgo, Javier
AU - Rosenhahn, Bodo
PY - 2015/4/16
Y1 - 2015/4/16
N2 - Regression-based Super-Resolution (SR) addresses the upscaling problem by learning a mapping function (i.e. regressor) from the low-resolution to the high-resolution manifold. Under the locally linear assumption, this complex non-linear mapping can be properly modeled by a set of linear regressors distributed across the manifold. In such methods, most of the testing time is spent searching for the right regressor within this trained set. In this paper we propose a novel inverse-search approach for regression-based SR. Instead of performing a search from the image to the dictionary of regressors, the search is done inversely from the regressors’ dictionary to the image patches. We approximate this framework by applying spherical hashing to both image and regressors, which reduces the inverse search into computing a trained function. Additionally, we propose an improved training scheme for SR linear regressors which improves perceived and objective quality. By merging both contributions we improve speed and quality compared to the stateof- the-art.
AB - Regression-based Super-Resolution (SR) addresses the upscaling problem by learning a mapping function (i.e. regressor) from the low-resolution to the high-resolution manifold. Under the locally linear assumption, this complex non-linear mapping can be properly modeled by a set of linear regressors distributed across the manifold. In such methods, most of the testing time is spent searching for the right regressor within this trained set. In this paper we propose a novel inverse-search approach for regression-based SR. Instead of performing a search from the image to the dictionary of regressors, the search is done inversely from the regressors’ dictionary to the image patches. We approximate this framework by applying spherical hashing to both image and regressors, which reduces the inverse search into computing a trained function. Additionally, we propose an improved training scheme for SR linear regressors which improves perceived and objective quality. By merging both contributions we improve speed and quality compared to the stateof- the-art.
UR - http://www.scopus.com/inward/record.url?scp=84983609925&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-16811-1_23
DO - 10.1007/978-3-319-16811-1_23
M3 - Conference contribution
AN - SCOPUS:84983609925
VL - 9005
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 346
EP - 359
BT - ACCV 2014
T2 - 12th Asian Conference on Computer Vision, ACCV 2014
Y2 - 1 November 2014 through 5 November 2014
ER -