Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2021

Abstract

Image Super-Resolution (SR) is an important class of image processing techniqueso enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning approaches. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future directions and open issues which should be further addressed by the community in the future.

Keywords

Image super-resolution, deep learning, convolutional neural networks (CNN), Generative adversarial nets (GAN)

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

43

Issue

10

First Page

3365

Last Page

3387

ISSN

0162-8828

Identifier

10.1109/TPAMI.2020.2982166

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TPAMI.2020.2982166

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