Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review |
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Authors: | Kui Fu Jiansheng Peng Hanxiao Zhang Xiaoliang Wang Frank Jiang |
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Affiliation: | 1.School of Physics and Mechanical and Electronic Engineering, Hechi University, Yizhou, 546300, China.
2 School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou,
545006, China.
3 School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan,
411100, China.
4 School of Information Technology, Deakin University, Geelong, VIC 3220, Australia. |
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Abstract: | 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. |
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Keywords: | Single image super-resolution generative adversarial networks deep learning computer vision |
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