Sparse Multi-modal probabilistic Latent Semantic Analysis for Single-Image Super-Resolution
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comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Title
Sparse Multi-modal probabilistic Latent Semantic Analysis for Single-Image Super-ResolutionDate
2018Publisher
ElsevierISSN
0165-1684Bibliographic citation
FERNANDEZ-BELTRAN, Ruben; PLA, Filiberto. Sparse multi-modal probabilistic latent semantic analysis for single-image super-resolution. Signal Processing, 2018, vol. 152, p. 227-237Type
info:eu-repo/semantics/articlePublisher version
https://www.sciencedirect.com/science/article/pii/S0165168418301944Version
info:eu-repo/semantics/submittedVersionSubject
Abstract
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. [-]
Is part of
Signal Processing, 2018, vol. 152, p. 227-237Investigation project
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.Rights
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info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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