[1] [1] Li X, Orchard M T. New edge-directed interpolation[J]. IEEE Transactions on Image Processing, 2001, 10(10): 1521–1527.

[2] [2] Zhang L, Wu X L. An edge-guided image interpolation algo-rithm via directional filtering and data fusion[J]. IEEE Transac-tions on Image Processing, 2006, 15(8): 2226–2238.

[3] [3] Dai S Y, Han M, XuW, et al. SoftCuts: a soft edge smoothness prior for color image super-resolution[J]. IEEE Transactions on Image Processing, 2009, 18(5): 969–981.

[4] [4] Sun J, Xu Z B, Shum H Y. Image super-resolution using gra-dient profile prior[C]//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008: 1–8.

[7] [7] Timofte R, De Smet V, Van Gool L. Anchored neighborhood regression for fast example-based super-resolution[C]// Pro-ceedings of 2013 IEEE International Conference on Computer Vision, 2013: 1920–1927.

[8] [8] Timofte R, De Smet V, Van Gool L. A+: adjusted anchored neighborhood regression for fast super-resolution[M]//Cremers D, Reid I, Saito H, et al. Computer Vision--ACCV 2014. Cham: Springer, 2014: 111–126.

[9] [9] Dong C,Loy C C, He K M, et al. Learning a deep convolutional network for image super-resolution[C]//Proceedings of the 13th European Conference on Computer Vision, 2014: 184–199.

[10] [10] Dong C, Loy C C, He K M, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295–307.

[11] [11] Yang J C, Wright J, Huang T S, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11): 2861–2873.

[12] [12] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Rec-ognition, 2016: 1646–1654.

[13] [13] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional net-work for image super-resolution[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1637–1645.

[14] [14] Zhang K, Zuo W M, Gu S H, et al. Learning deep CNN De-noiser prior for image restoration[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2808–2817.

[15] [15] Shi W Z, Jiang F, Zhao D B. Single image super-resolution with dilated convolution based multi-scale information learning in-ception module[C]//Proceedings of 2017 IEEE International Conference on Image Processing, 2017: 977–981.

[16] [16] Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial net-work[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 105–114.

[17] [17] Tai Y, Yang J, Liu X M. Image super-resolution via deep recur-sive residual network[C]//Proceedings of 2017 IEEE Confe-rence on Computer Vision and Pattern Recognition, 2017:2790–2798.

[18] [18] Mao X J, Shen C H, Yang Y B. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems, 2016: 2810–2818.

[19] [19] Tai Y, Yang J, Liu X M, et al. MemNet: a persistent memory network for image restoration[C]//Proceedings of 2017 IEEE International Conference on Computer Vision, 2017: 4549–4557.

[20] [20] Dong C, Loy C C, Tang X O. Accelerating the super-resolution convolutional neural network[C]//Proceedings of the 14th Eu-ropean Conference on Computer Vision, 2016: 391–407.

[21] [21] Shi W Z, Caballero J, Huszár F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convo-lutional neural network[C]//Proceedings of 2016 IEEE Confe-rence on Computer Vision and Pattern Recognition, 2016: 1874–1883.

[22] [22] Lai W S, Huang J B, Ahuja N, et al. Deep Laplacian pyramid networks for fast and accurate super-resolution[C]// Proceed-ings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 5835–5843.

[23] [23] Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of 2017 IEEE Confe-rence on Computer Vision and Pattern Recognition, 2017: 2261–2269.

[24] [24] Chen Y P, Li J A, Xiao H X, et al. Dual path net-works[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 4470–4478.

[25] [25] Hu J, Shen L, Albanie S, et al. Squeeze-and-excitation net-works[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, doi: 10.1109/TPAMI.2019.2913372.

[26] [26] Timofte R, Agustsson E, van Gool L, et al. NTIRE 2017 chal-lenge on single image super-resolution: methods and re-sults[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017: 1110–1121.

[27] [27] Bevilacqua M, Roumy A, Guillemot C, et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C]//Proceedings of the British Machine Vision Conference, 2012: 135.1–135.10.

[28] [28] Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations[C]//Proceedings of the 7th International Conference on Curves and Surfaces, 2010: 711–730.

[29] [29] Huang J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 5197–5206.

[30] [30] Martin D, Fowlkes C, Tal D, et al. A database of human seg-mented natural images and its application to evaluating seg-mentation algorithms and measuring ecological statis-tics[C]//Proceedings of the 8th IEEE International Conference on Computer Vision, 2001: 416–423.

[31] [31] He K M, Zhang X Y, Ren S Q, et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classifica-tion[C]//Proceedings of 2015 IEEE International Conference on Computer Vision, 2015: 1026–1034.

[32] [32] Zhao H, Gallo O, Frosio I, et al. Loss functions for neural net-works for image processing[OL]. arXiv:1511.08861[cs.CV], 2015.

[33] [33] Bruhn A, Weickert J, Schn.rr C. Lucas/Kanade meets Horn/Schunck: combining local and global optic flow me-thods[J]. International Journal of Computer Vision, 2005, 61(3): 211–231.