Sparse Multi-modal probabilistic Latent Semantic Analysis for Single-Image Super-Resolution
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TitleSparse Multi-modal probabilistic Latent Semantic Analysis for Single-Image Super-Resolution
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. [-]
Investigation projectThis 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.
Bibliographic citationFERNANDEZ-BELTRAN, Ruben; PLA, Filiberto. Sparse multi-modal probabilistic latent semantic analysis for single-image super-resolution. Signal Processing, 2018, vol. 152, p. 227-237