Laser & Optoelectronics Progress, Volume. 59, Issue 4, 0420002(2022)
Super-Resolution Reconstruction Algorithm of Images Based on Improved Enhanced Super-Resolution Generative Adversarial Network
[8] Hu S Y, Wang G D, Zhao Y et al. Image super-resolution network based on dense connection and squeeze module[J]. Laser & Optoelectronics Progress, 56, 201005(2019).
[14] Li Y H, Mu X, Zhu Y L et al. Super resolution image restoration and reconstruction of deep generative countermeasure network[J]. Journal of Xi’an University of Technology, 35, 1-8(2021).
[15] Peng Y F, Zhang P J, Gao Y et al. Attention fusion generative adversarial network for single-image super-resolution reconstruction[J]. Laser & Optoelectronics Progress, 58, 2010012(2021).
[16] Chen Z H, Wu H B, Pei H D et al. Image super-resolution reconstruction method based on self-attention deep network[J]. Laser & Optoelectronics Progress, 58, 0410013(2021).
[17] Zha T B, Luo L, Yang K et al. Image reconstruction algorithm based on improved super-resolution generative adversarial network[J]. Laser & Optoelectronics Progress, 58, 0810005(2021).
[22] Radford A, Metz L, Chintala et al. Unsupervised representation learning with deep convolutional generative adversarial networks[C](2016).
[23] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C](2015).
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Yuanxue Xin, Fengting Zhu, Pengfei Shi, Xin Yang, Runkang Zhou. Super-Resolution Reconstruction Algorithm of Images Based on Improved Enhanced Super-Resolution Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0420002
Category: Optics in Computing
Received: Jul. 21, 2021
Accepted: Sep. 13, 2021
Published Online: Feb. 15, 2022
The Author Email: Pengfei Shi (shipf@hhu.edu.cn)