UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Deep Learning for Single Image Super-Resolution: A Brief Review

Yang, W; Zhang, X; Tian, Y; Wang, W; Xue, J-H; Liao, Q; (2019) Deep Learning for Single Image Super-Resolution: A Brief Review. IEEE Transactions on Multimedia 10.1109/tmm.2019.2919431. (In press). Green open access

[img]
Preview
Text
XuechenZhang-review-TMM-2019-UCL.pdf - Accepted version

Download (12MB) | Preview

Abstract

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that aims to obtain a high- resolution (HR) output from one of its low-resolution (LR) versions. Recently, powerful deep learning algorithms have been applied to SISR and have achieved state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods and group them into two categories according to their contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is first established, and several critical limitations of the baseline are summarized. Then, representative works on overcoming these limitations are presented based on their original content, as well as our critical exposition and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally, we conclude this review with some current challenges and future trends in SISR that leverage deep learning algorithms.

Type: Article
Title: Deep Learning for Single Image Super-Resolution: A Brief Review
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/tmm.2019.2919431
Publisher version: https://doi.org/10.1109/TMM.2019.2919431
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Single image super-resolution, deep learning, neural networks, objective function
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10078216
Downloads since deposit
48Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item