Sparse Multi-modal probabilistic Latent Semantic Analysis for Single-Image Super-Resolution
Visualitza/
Metadades
Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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INVESTIGACIONMetadades
Títol
Sparse Multi-modal probabilistic Latent Semantic Analysis for Single-Image Super-ResolutionData de publicació
2018Editor
ElsevierISSN
0165-1684Cita bibliogràfica
FERNANDEZ-BELTRAN, Ruben; PLA, Filiberto. Sparse multi-modal probabilistic latent semantic analysis for single-image super-resolution. Signal Processing, 2018, vol. 152, p. 227-237Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://www.sciencedirect.com/science/article/pii/S0165168418301944Versió
info:eu-repo/semantics/submittedVersionParaules clau / Matèries
Resum
This paper presents a novel single-image super-resolution (SR) approach
based on latent topics in order to take advantage of the semantics pervading
the topic space when super-resolving images. Image semantics has ... [+]
This paper presents a novel single-image super-resolution (SR) approach
based on latent topics in order to take advantage of the semantics pervading
the topic space when super-resolving images. Image semantics has shown to
be useful to relieve the ill-posed nature of the SR problem, however the most
accepted clustering-based approach used to define semantic concepts limits the
capability of representing complex visual relationships. The proposed approach
provides a new probabilistic perspective where the SR process is performed
according to the semantics encapsulated by a new topic model, the Sparse Multimodal
probabilistic Latent Semantic Analysis (sMpLSA). Firstly, the sMpLSA
model is formulated. Subsequently, a new SR framework based on sMpLSA is
defined. Finally, an experimental comparison is conducted using seven learningbased
SR methods over three different image datasets. Experiments reveal the
potential of latent topics in SR by reporting that the proposed approach is able
to provide a competitive performance. [-]
Publicat a
Signal Processing, 2018, vol. 152, p. 227-237Proyecto de investigación
This work was supported by the Spanish Ministry of Economy under the projects ESP2013-48458-C4-3-P and ESP2016-79503-C2-2-P, by Generalitat Valenciana through the PROMETEO-II/2014/062 project and the APOSTD/2017/007 contract, and by Universitat Jaume I under the P11B2014-09 project.Drets d'accés
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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