[1] Park S C, Park M K, Kang M G. Super-resolution image reconstruction: a technical overview[J]. IEEE Signal Processing Magazine, 20, 21-36(2003).
[3] Dong C, Loy C C, He K M et al. Learning a deep convolutional network for image super-resolution[M]. ∥Fleet D, Pajdla T, Schiele B,
[4] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 26-July 1, 2016, Las Vegas, Nevada. New York: IEEE, 1637-1645(2016).
[5] Simonyan K. -04-10)[2019-05-12]. https:∥arxiv., org/abs/1409, 1556(2015).
[6] Shi W Z, Caballero J, Huszar F et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 1874-1883(2016).
[7] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 1132-1140(2017).
[8] Lai W S, Huang J B, Ahuja N et al. Deep Laplacian pyramid networks for fast and accurate super-resolution. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 5835-5843(2017).
[9] Sønderby C K, Caballero J, Theis L et al. -02-21)[2019-05-12]. https:∥arxiv., org/abs/1610, 04490(2017).
[10] Dahl R, Norouzi M, Shlens J. Pixel recursive super resolution. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 5439-5448(2017).
[11] Goodfellow I J, Pouget-Abadie J, Mirza M et al. Generative adversarial nets. [C]∥Proceedings of the 27th International Conference on Neural Information Processing Systems, December 4-9, 2017, Long Beach, CA, USA. Canada: NIPS(2017).
[12] 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), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 4681-4690(2017).
[13] Wang X T, Yu K, Wu S X et al. ESRGAN: enhanced super-resolution generative adversarial networks[M]. ∥Leal-Taixé L, Roth S. Computer vision-ECCV 2018 Workshops. Lecture notes in computer science. Cham: Springer, 11133, 63-79(2019).
[14] Nguyen T D, Le T, Vu H et al. Dual discriminator generative adversarial nets. [C]∥Proceedings of the 27th International Conference on Neural Information Processing Systems, December 4-9, 2017, Long Beach, CA, USA. Canada: NIPS(2017).
[15] He K M, Zhang X Y, Ren S Q et al. Identity mappings in deep residual networks[M]. ∥Leibe B, Matas J, Sebe N,
[16] Gulrajani I, Ahmed F, Arjovsky M et al. Improved training of Wasserstein GANs. [C]∥Proceedings of the 27th International Conference on Neural Information Processing Systems, December 4-9, 2017, Long Beach, CA, USA. Canada: NIPS(2017).
[17] Wang Z, Bovik A C. Mean squared error: love it or leave it? A new look at signal fidelity measures[J]. IEEE Signal Processing Magazine, 26, 98-117(2009).
[18] Zhao H, Gallo O, Frosio I et al. Loss functions for image restoration with neural networks[J]. IEEE Transactions on Computational Imaging, 3, 47-57(2017).
[19] Bruhn A, Weickert J, Schnörr C. Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods[J]. International Journal of Computer Vision, 61, 211-231(2005).
[20] Wu B Z, Duan H D, Liu Z C et al. -12-20)[2019-05-12]. https:∥arxiv., org/abs/1712, 05927(2017).
[21] Liu G L, Reda F A, Shih K J et al. Image inpainting for irregular holes using partial convolutions[M]. ∥Ferrari V, Hebert M, Sminchisescu C,
[22] Agustsson E, Timofte R. NTIRE 2017 challenge on single image super-resolution: dataset and study. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 1122-1131(2017).
[23] Bevilacqua M, Roumy A, Guillemot C et al. Neighbor embedding based single-image super-resolution using Semi-Nonnegative Matrix Factorization. [C]∥2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 25-30, 2012, Kyoto, Japan. New York: IEEE, 1289-1292(2012).
[24] Yuan Y, Liu S Y, Zhang J W et al. Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks. [C]∥2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 18-22, 2018, Salt Lake City, Utah. New York: IEEE, 814-823(2018).
[25] Martin D, Fowlkes C, Tal D et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. [C]∥Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, July 7-14, 2001, Vancouver, BC, Canada. New York: IEEE, 416-423(2001).
[26] Zhang Y L, Tian Y P, Kong Y et al. -12-25)[2019-05-12]. https:∥arxiv., org/abs/1812, 10477(2018).