research-article

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

Authors:

Qingmin LiaoAuthors Info & Claims

IEEE Transactions on Multimedia, Volume 21, Issue 12

Pages 3106 - 3121

https://doi.org/10.1109/TMM.2019.2919431

Published: 01 December 2019 Publication History

Abstract

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that aims to obtain a high-resolution output from one of its low-resolution 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.

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Super-resolution (SR), aiming to super-resolve degraded low-resolution image to recover the corresponding high-resolution counterpart, is an important and challenging task in computer vision, and with various applications. The emergence of deep ...
Highlights
- This paper provides a comprehensive survey on deep-learning-based super-resolution methods along with their applications and limitations.
- This paper categorizes the methods from an application perspective and a taxonomy is proposed.
Image super-resolution: use of self-learning and gabor prior
ACCV'12: Proceedings of the 11th Asian conference on Computer Vision - Volume Part III

Recent approaches on single image super-resolution (SR) have attempted to exploit self-similarity to avoid the use of multiple images. In this paper, we propose an SR method based on self-learning and Gabor prior. Given a low resolution (LR) test image ...

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cover image IEEE Transactions on Multimedia

IEEE Transactions on Multimedia Volume 21, Issue 12

Dec. 2019

258 pages

ISSN:1520-9210

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1520-9210 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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IEEE Press

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Published: 01 December 2019

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Su HLi YXu YFu XLiu S(2025)A review of deep-learning-based super-resolutionPattern Recognition10.1016/j.patcog.2024.110935157:COnline publication date: 1-Jan-2025
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Gopalakrishnan SChoudhury A(2025)A ‘deep’ review of video super-resolutionImage Communication10.1016/j.image.2024.117175129:COnline publication date: 7-Jan-2025
https://dl.acm.org/doi/10.1016/j.image.2024.117175
Jessica LArafat NLim WChan WKong ASalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Finite volume features, global geometry representations, and residual training for deep learning-based CFD simulationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692935(21555-21576)Online publication date: 21-Jul-2024
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Deng HXu ZDuan YWu XShu WDeng LSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Exploring the low-pass filtering behavior in image super-resolutionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692489(10553-10573)Online publication date: 21-Jul-2024
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Park HGrama Srinivasan SCong MKim DKim BSwartz JMuseth KSifakis E(2024)Near-realtime Facial Animation by Deep 3D Simulation Super-ResolutionACM Transactions on Graphics10.1145/367068743:5(1-20)Online publication date: 9-Aug-2024
https://dl.acm.org/doi/10.1145/3670687
Ma CTan WZhou SYan BCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Learning Cross-Spectral Prior for Image Super-ResolutionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681169(1447-1455)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681169
Moon JLee HChu JPark DHong SSeo HJeong DKong SHam MRoychoudhury APaiva AAbreu RStorey MAniche MNagappan N(2024)A New Frontier of AI: On-Device AI Training and PersonalizationProceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice10.1145/3639477.3639716(323-333)Online publication date: 14-Apr-2024
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Chu HLong LGuo JYuan HDeng L(2024)Implicit function‐based continuous representation for meticulous segmentation of cracks from high‐resolution imagesComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1305239:4(539-558)Online publication date: 13-Feb-2024
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Prasad Vvan Sloun RElzen SVilanova APezzotti N(2024)The Transform-and-Perform Framework: Explainable Deep Learning Beyond ClassificationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.321924830:2(1502-1515)Online publication date: 1-Feb-2024
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Sun WChen Z(2024)Learning Many-to-Many Mapping for Unpaired Real-World Image Super-Resolution and DownscalingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.342854646:12(9874-9889)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TPAMI.2024.3428546
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