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dc.contributor.authorPérez-Pellitero, Eduardo
dc.contributor.authorSalvador, Jordi
dc.contributor.authorRuiz Hidalgo, Javier
dc.contributor.authorRosenhahn, Bodo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2016-07-21T12:54:31Z
dc.date.available2016-07-21T12:54:31Z
dc.date.issued2016-03-31
dc.identifier.citationPérez-Pellitero, E., Salvador, J., Ruiz-Hidalgo, J., Rosenhahn, B. Antipodally invariant metrics for fast regression-based super-resolution. "IEEE transactions on image processing", 31 Març 2016, vol. 25, núm. 6, p. 2456-2468.
dc.identifier.issn1057-7149
dc.identifier.urihttp://hdl.handle.net/2117/89049
dc.description.abstractDictionary-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.
dc.format.extent13 p.
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
dc.subject.lcshImage processing -- Digital techniques
dc.subject.otherAntipodes
dc.subject.otherRegression
dc.subject.otherSpherical Hashing
dc.subject.otherSuper-Resolution
dc.subject.otherSuper-resolution
dc.subject.otherAntipodes
dc.subject.otherRegression
dc.subject.otherSpherical hashing
dc.titleAntipodally invariant metrics for fast regression-based super-resolution
dc.typeArticle
dc.subject.lemacImatges -- Processament -- Tècniques digitals
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.identifier.doi10.1109/TIP.2016.2549362
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7445242
dc.rights.accessOpen Access
drac.iddocument17726262
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorPérez-Pellitero, E., Salvador, J., Ruiz-Hidalgo, J., Rosenhahn, B.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameIEEE transactions on image processing
upcommons.citation.volume25
upcommons.citation.number6
upcommons.citation.startingPage2456
upcommons.citation.endingPage2468


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