This repo contains the codes for the paper: Sparse 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 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 Multi-modal 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 learning-based 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.
./codes/smplsa_l/run_pLSA.sh is the script for sMpLSA-L stage.
./codes/smplsa_h_tst/run_pLSA.sh is the script for sMpLSA-L and sMpLSA-tst stages.
@article{fernandez2018sparse,
title={Sparse multi-modal probabilistic latent semantic analysis for single-image super-resolution},
author={Fernandez-Beltran, Ruben and Pla, Filiberto},
journal={Signal Processing},
volume={152},
pages={227--237},
year={2018},
publisher={Elsevier}
}
[1] Yue, L., Shen, H., Li, J., Yuan, Q., Zhang, H., & Zhang, L. (2016). Image super-resolution: The techniques, applications, and future. Signal processing, 128, 389-408.