Deep learning based single image super-resolution: a survey.

Ha, Viet Khanh; Ren, Jin-Chang; Xu, Xin-Ying; Zhao, Sophia; Xie, Gang; Masero, Valentin; Hussain, Amir

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.

Citation

HA, V.K., REN, J.-C., XU, X.-Y., ZHAO, S., XIE, G., MASERO, V. and HUSSAIN, A. 2019. Deep learning based single image super-resolution: a survey. International journal of automation and computing [online], 16(4), pages 413-426. Available from: https://doi.org/10.1007/s11633-019-1183-x

• Journal Article Type: Article
• Acceptance Date: Apr 19, 2019
• Online Publication Date: Jul 19, 2019
• Publication Date: Aug 31, 2019
• Deposit Date: May 6, 2022
• Publicly Available Date: Jun 30, 2022
• Journal: International Journal of Automation and Computing
• Print ISSN: 1476-8186
• Electronic ISSN: 1751-8520
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 4
• Pages: 413-426
• DOI: https://doi.org/10.1007/s11633-019-1183-x
• Keywords: Image super-resolution; Convolutional neural network; High-resolution image; Low-resolution image; Deep learning
• Public URL: https://rgu-repository.worktribe.com/output/1085637