Details
| Original language | English |
|---|---|
| Article number | 7445242 |
| Pages (from-to) | 2456-2468 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 25 |
| Issue number | 6 |
| Publication status | Published - Jun 2016 |
Abstract
Dictionary-based super-resolution (SR) algorithms usually select dictionary atoms based on the distance or similarity metrics. Although the optimal selection of the nearest neighbors is of central importance for such methods, the impact of using proper metrics for SR has been overlooked in literature, mainly due to the vast usage of Euclidean distance. In this paper, we present a very fast regression-based algorithm, which builds on the densely populated anchored neighborhoods and sublinear search structures. We perform a study of the nature of the features commonly used for SR, observing that those features usually lie in the unitary hypersphere, where every point has a diametrically opposite one, i.e., its antipode, with same module and angle, but the opposite direction. Even though, we validate the benefits of using antipodally invariant metrics, most of the binary splits use Euclidean distance, which does not handle antipodes optimally. In order to benefit from both the worlds, we propose a simple yet effective antipodally invariant transform that can be easily included in the Euclidean distance calculation. We modify the original spherical hashing algorithm with this metric in our antipodally invariant spherical hashing scheme, obtaining the same performance as a pure antipodally invariant metric. We round up our contributions with a novel feature transform that obtains a better coarse approximation of the input image thanks to iterative backprojection. The performance of our method, which we named antipodally invariant SR, improves quality (Peak Signal to Noise Ratio) and it is faster than any other state-of-the-art method.
Keywords
- antipodes, regression, spherical hashing, Super-resolution
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
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In: IEEE Transactions on Image Processing, Vol. 25, No. 6, 7445242, 06.2016, p. 2456-2468.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Antipodally invariant metrics for fast regression-based super-resolution
AU - Perez-Pellitero, Eduardo
AU - Salvador, Jordi
AU - Ruiz-Hidalgo, Javier
AU - Rosenhahn, Bodo
N1 - Funding information: This work was supported in part by the Spanish Ministerio de Economia y Competitividad through the European Regional Development Fund under Project TEC2013-43935-R, in part by the European Research Council within the Starting Grant Dynamic MinVIP, and in part the Cluster of Excellence Rebirth.
PY - 2016/6
Y1 - 2016/6
N2 - Dictionary-based super-resolution (SR) algorithms usually select dictionary atoms based on the distance or similarity metrics. Although the optimal selection of the nearest neighbors is of central importance for such methods, the impact of using proper metrics for SR has been overlooked in literature, mainly due to the vast usage of Euclidean distance. In this paper, we present a very fast regression-based algorithm, which builds on the densely populated anchored neighborhoods and sublinear search structures. We perform a study of the nature of the features commonly used for SR, observing that those features usually lie in the unitary hypersphere, where every point has a diametrically opposite one, i.e., its antipode, with same module and angle, but the opposite direction. Even though, we validate the benefits of using antipodally invariant metrics, most of the binary splits use Euclidean distance, which does not handle antipodes optimally. In order to benefit from both the worlds, we propose a simple yet effective antipodally invariant transform that can be easily included in the Euclidean distance calculation. We modify the original spherical hashing algorithm with this metric in our antipodally invariant spherical hashing scheme, obtaining the same performance as a pure antipodally invariant metric. We round up our contributions with a novel feature transform that obtains a better coarse approximation of the input image thanks to iterative backprojection. The performance of our method, which we named antipodally invariant SR, improves quality (Peak Signal to Noise Ratio) and it is faster than any other state-of-the-art method.
AB - Dictionary-based super-resolution (SR) algorithms usually select dictionary atoms based on the distance or similarity metrics. Although the optimal selection of the nearest neighbors is of central importance for such methods, the impact of using proper metrics for SR has been overlooked in literature, mainly due to the vast usage of Euclidean distance. In this paper, we present a very fast regression-based algorithm, which builds on the densely populated anchored neighborhoods and sublinear search structures. We perform a study of the nature of the features commonly used for SR, observing that those features usually lie in the unitary hypersphere, where every point has a diametrically opposite one, i.e., its antipode, with same module and angle, but the opposite direction. Even though, we validate the benefits of using antipodally invariant metrics, most of the binary splits use Euclidean distance, which does not handle antipodes optimally. In order to benefit from both the worlds, we propose a simple yet effective antipodally invariant transform that can be easily included in the Euclidean distance calculation. We modify the original spherical hashing algorithm with this metric in our antipodally invariant spherical hashing scheme, obtaining the same performance as a pure antipodally invariant metric. We round up our contributions with a novel feature transform that obtains a better coarse approximation of the input image thanks to iterative backprojection. The performance of our method, which we named antipodally invariant SR, improves quality (Peak Signal to Noise Ratio) and it is faster than any other state-of-the-art method.
KW - antipodes
KW - regression
KW - spherical hashing
KW - Super-resolution
UR - http://www.scopus.com/inward/record.url?scp=84964627183&partnerID=8YFLogxK
U2 - 10.1109/tip.2016.2549362
DO - 10.1109/tip.2016.2549362
M3 - Article
AN - SCOPUS:84964627183
VL - 25
SP - 2456
EP - 2468
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
IS - 6
M1 - 7445242
ER -