survey

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

Authors:

Longguang Wang,

Tieyong ZengAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 10

Article No.: 249, Pages 1 - 40

https://doi.org/10.1145/3659100

Published: 14 May 2024 Publication History

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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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 10

October 2024

954 pages

EISSN:1557-7341

DOI:10.1145/3613652

Editors:
David Atienza
Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
,
Michela Milano
University of Bologna, Italy

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Publication History

Published: 14 May 2024

Online AM: 13 April 2024

Accepted: 10 April 2024

Revised: 03 April 2024

Received: 06 May 2023

Published in CSUR Volume 56, Issue 10

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https://doi.org/10.3390/electronics13163142
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