[1] Chen H G, He X H, Teng Q Z, et al. Single image super resolution using local smoothness and nonlocal self-similarity priors[J]. Signal Processing: Image Communication, 2016, 43: 68-81.

[2] OktayO, Bai WJ, LeeM, et al.Multi-input cardiac image super-resolution using convolutional neural networks[M] ∥Lecture Notes in Computer Science. Cham: Springer International Publishing, 2016: 246- 254.

[3] Park S C, Park M K, Kang M G. Super-resolution image reconstruction: a technical overview[J]. IEEE Signal Processing Magazine, 2003, 20(3): 21-36.

[4] Schultz R R, Stevenson R L. Extraction of high-resolution frames from video sequences[J]. IEEE Transactions on Image Processing, 1996, 5(6): 996-1011.

[5] ChangH, Yeung DY, Xiong YM. Super-resolution through neighbor embedding[C]∥Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004.

[6] Irani M, Peleg S. Improving resolution by image registration[J]. CVGIP: Graphical Models and Image Processing, 1991, 53(3): 231-239.

[7] Stark H, Oskoui P. High-resolution image recovery from image-plane arrays, using convex projections[J]. Journal of the Optical Society of America A, 1989, 6(11): 1715-1726.

[8] ChangH, Yeung DY, Xiong YM. Super-resolution through neighbor embedding[C]∥Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004: 275- 282.

[9] Yang JC, WrightJ, HuangT, et al. Image super-resolution as sparse representation of raw image patches[C]∥2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008: 1- 8.

[10] DongC, Loy CC, He KM, et al.Learning a deep convolutional network for image super-resolution[M] ∥Computer Vision-ECCV 2014. Cham: Springer International Publishing, 2014: 184- 199.

[11] KimJ, Lee JK, Lee KM. Accurate image super-resolution using very deep convolutional networks[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 1646- 1654.

[12] 袁昆鹏, 席志红. 基于深度跳跃级联的图像超分辨率重建[J]. 光学学报, 2019, 39(7): 0715003.

    Yuan K P, Xi Z H. Image super resolution based on depth jumping cascade[J]. Acta Optica Sinica, 2019, 39(7): 0715003.

[13] Dong C, Loy C C, Tang X O. Accelerating the super-resolution convolutional neural network[J]. Computer Vision, 2016: 391-407.

[14] Shi WZ, CaballeroJ, HuszárF, 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), 2016: 1874- 1883.

[15] Lai WS, Huang JB, AhujaN, et al. Deep laplacian pyramid networks for fast and accurate super-resolution[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: 5835- 5843.

[16] Lai W S, Huang J B, Ahuja N, et al. Fast and accurate image super-resolution with deep Laplacian pyramid networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(11): 2599-2613.

[17] SchroffF, KalenichenkoD, PhilbinJ. FaceNet: a unified embedding for face recognition and clustering[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015: 815- 823.

[18] 吴磊, 吕国强, 薛治天, 等. 基于多尺度递归网络的图像超分辨率重建[J]. 光学学报, 2019, 39(6): 0610001.

    Wu L, Lü G Q, Xue Z T, et al. Super-resolution reconstruction of images based on multi-scale recursive network[J]. Acta Optica Sinica, 2019, 39(6): 0610001.

[19] Kingma DP, Ba J. Adam: a method for stochastic optimization[EB/OL]. ( 2017-01-30)[2020-01-07]. https: ∥arxiv.org/abs/1412. 6980.

[20] 王昌书. 基于机器视觉的手机曲面玻璃典型缺陷检测研究[D]. 广州: 华南理工大学, 2019: 42- 43.

    Wang CS. Machine vision based typical defects inspection for curved glass of mobile phone[D]. Guangzhou: South China University of Technology, 2019: 42- 43.

[21] Zhao H, Gallo O, Frosio I, et al. Loss functions for image restoration with neural networks[J]. IEEE Transactions on Computational Imaging, 2017, 3(1): 47-57.

[22] 张雷, 胡博文, 张宁, 等. 图像超分辨率全局残差递归网络[J]. 计算机科学, 2019, 46(S1): 230-233.

    Zhang L, Hu B W, Zhang N, et al. Global residual recursive network for image super-resolution[J]. Computer Science, 2019, 46(S1): 230-233.

[23] 余景景. 基于正则化的生物发光断层成像重建算法研究[D]. 西安: 西安电子科技大学, 2011: 55- 56.

    Yu JJ. Regularization based reconstruction algorithms for bioluminescence tomography[D]. Xi'an:Xidian University, 2011: 55- 56.

[24] 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.

[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[J]. Proceedings of International Conference on Computer Vision, 2002: 416-423.

[26] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

[27] Ye Y X, Shan J, Bruzzone L, et al. Robust registration of multimodal remote sensing images based on structural similarity[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(5): 2941-2958.