survey

A Systematic Survey of Deep Learning-Based Single-Image Super-Resolution

Authors:
Juncheng Li

Shanghai University, Shanghai, China

Shanghai University, Shanghai, China

0000-0001-7314-6754
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,
Zehua Pei

The Chinese University of Hong Kong, Hong Kong, China

The Chinese University of Hong Kong, Hong Kong, China

0000-0002-2884-2459
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,
Wenjie Li

Beijing University of Posts and Telecommunications, Beijing, China

Beijing University of Posts and Telecommunications, Beijing, China

0000-0003-3086-5307
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,
Guangwei Gao

Nanjing University of Posts and Telecommunications, Nanjing, China

Nanjing University of Posts and Telecommunications, Nanjing, China

0000-0002-3950-1844
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,
Longguang Wang

Aviation University of Air Force, Changchun, China

Aviation University of Air Force, Changchun, China

0000-0003-0429-0263
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,
Yingqian Wang

National University of Defense Technology, Changsha, China

National University of Defense Technology, Changsha, China

0000-0002-9081-6227
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,
Tieyong Zeng

The Chinese University of Hong Kong, Hong Kong, China

The Chinese University of Hong Kong, Hong Kong, China

0000-0002-0688-202X
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Authors Info & Claims

ACM Computing Surveys Volume 56 Issue 10Article No.: 249pp 1–40https://doi.org/10.1145/3659100

Published:14 May 2024Publication History

ACM Computing Surveys

Abstract

Single-image super-resolution (SISR) is an important task in image processing, which aims to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and has achieved promising results with the help of deep learning (DL). In this survey, we give an overview of DL-based SISR methods and group them according to their design targets. Specifically, we first introduce the problem definition, research background, and the significance of SISR. Secondly, we introduce some related works, including benchmark datasets, upsampling methods, optimization objectives, and image quality assessment methods. Thirdly, we provide a detailed investigation of SISR and give some domain-specific applications of it. Fourthly, we present the reconstruction results of some classic SISR methods to intuitively know their performance. Finally, we discuss some issues that still exist in SISR and summarize some new trends and future directions. This is an exhaustive survey of SISR, which can help researchers better understand SISR and inspire more exciting research in this field. An investigation project for SISR is provided at https://github.com/CV-JunchengLi/SISR-Survey.

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Index Terms

A Systematic Survey of Deep Learning-Based Single-Image Super-Resolution
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. General and reference
  1. Document types
    1. Surveys and overviews

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ACM Computing Surveys Volume 56, Issue 10
October 2024
325 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3613652
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Publication History
- Published: 14 May 2024
- Online AM: 13 April 2024
- Accepted: 10 April 2024
- Revised: 3 April 2024
- Received: 6 May 2023
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A Systematic Survey of Deep Learning-Based Single-Image Super-Resolution

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Single image super-resolution via self-similarity and low-rank matrix recovery

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A Systematic Survey of Deep Learning-Based Single-Image Super-Resolution

ACM Computing Surveys

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Single image super-resolution via self-similarity and low-rank matrix recovery

Texture enhancement for improving single-image super-resolution performance

Fast Learning-Based Single Image Super-Resolution

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