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Image super-resolution based on generative adversarial networks: A brief review

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Version 2 2024-06-21, 07:52
Version 1 2020-09-07, 09:21
journal contribution
posted on 2024-06-21, 07:52 authored by K Fu, J Peng, H Zhang, X Wang, F Jiang
Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the improved network structures and loss functions of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of different networks are analyzed by multiple comparative experiments. Finally, we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.

History

Journal

Computers, Materials and Continua

Volume

64

Pagination

1977-1997

Location

Henderson, Nev.

Open access

  • Yes

ISSN

1546-2218

eISSN

1546-2226

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Issue

3

Publisher

TECH SCIENCE PRESS