Infrared Technology, Volume. 43, Issue 4, 391(2021)
Terahertz Image Enhancement Based on Generative Adversarial Network
[7] [7] Ledig C, Theis L, Huszar F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017: 4681-4690.
[10] [10] Gross S, Wilber M. Training and investigating residual nets, online[EB/OL].[2016-02-04]. http://torch.ch/blog/.
[11] [11] Shi W, Caballero J, Huszar F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 1874-1883.
[12] [12] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[C]// International Conference on Learning Representations (ICLR), 2016: 1-16.
[13] [13] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C]//International Conference on Learning Representations (ICLR), 2015: 268-282.
[14] [14] Bruna J, Sprechmann P, Lecun Y. Super-resolution with deep convolutional sufficient statistics[C]//International Conference on Learning Representations (ICLR), 2016: 352-369.
[15] [15] Gatys L A, Ecker A S, Bethge M. Texture synthesis using convolutional neural networks[C]//Advances in Neural Information Processing Systems (NIPS), 2015: 262-270.
[16] [16] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in Neural Information Processing Systems (NIPS), 2014: 2672-2680.
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ZHANG Pengcheng, HE Mingxia, CHEN Shuo, ZHANG Hongzhen, ZHANG Xinxin. Terahertz Image Enhancement Based on Generative Adversarial Network[J]. Infrared Technology, 2021, 43(4): 391
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Received: Sep. 29, 2019
Accepted: --
Published Online: Aug. 26, 2021
The Author Email: Pengcheng ZHANG (1298216729@qq.com)
CSTR:32186.14.