@Article{cmc.2020.09882,
AUTHOR = {Kui Fu, Jiansheng Peng, Hanxiao Zhang, Xiaoliang Wang, Frank Jiang},
TITLE = {Image Super-Resolution Based on Generative Adversarial  Networks: A Brief Review},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1977--1997},
URL = {http://www.techscience.com/cmc/v64n3/39471},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.09882}
}