Deep learning based single image super-resolution : a survey

Ha, Viet Khanh and Ren, Jin Chang and Xu, Xin Ying and Zhao, Sophia and Xie, Gang and Masero, Valentin and Hussain, Amir (2019) Deep learning based single image super-resolution : a survey. International Journal of Automation and Computing, 16 (4). pp. 413-426. ISSN 1476-8186 (https://doi.org/10.1007/s11633-019-1183-x)

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Abstract

Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recently, deep learning techniques have emerged and blossomed, producing “the state-of-the-art” in many domains. Due to their capability in feature extraction and mapping, it is very helpful to predict high-frequency details lost in low-resolution images. In this paper, we give an overview of recent advances in deep learning-based models and methods that have been applied to single image super-resolution tasks. We also summarize, compare and discuss various models from the past and present for comprehensive understanding and finally provide open problems and possible directions for future research.

ORCID iDs

Ha, Viet Khanh, Ren, Jin Chang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194, Xu, Xin Ying, Zhao, Sophia, Xie, Gang, Masero, Valentin and Hussain, Amir;
CORE (COnnecting REpositories)