[6] DONG C, CHEN C L, HE K, et al. Learning a Deep Convolutional Network for Image Super-Resolution[M]//Computer Vision – ECCV Springer International Publishing, 2014: 184-199.

[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.

[9] Johnson J, Alahi A, LI F. Perceptual losses for real-time style transfer and super-resolution[C]//In European Conference on Computer Vision (ECCV), Springer, 2016: 694-711.

[10] Gross S, Wilber M. Training and investigating residual nets, online[EB/OL].[2016-02-04]. http://torch.ch/blog/.

[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] 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] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C]//International Conference on Learning Representations (ICLR), 2015: 268-282.

[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] 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] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in Neural Information Processing Systems (NIPS), 2014: 2672-2680.