[5] [5] Lozanov Y, Tzvetkova S. A methodology for processing of thermographic images for diagnostics of electrical equipment[C]//2019 11th Electrical Engineering Faculty Conference (BulEF). IEEE, 2019: 1-4.

[6] [6] LI Y, ZHAO K, REN F, et al. Research on super-resolution image reconstruction based on low-resolution infrared sensor[J]. IEEE Access, 2020, 8: 69186-69199.

[8] [8] B.tz M, Eichenseer A, Kaup A. Multi-image super-resolution using a dual weighting scheme based on Voronoi tessellation[C]//2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016: 2822-2826.

[9] [9] WANG L, LIN Z, DENG X, et al. Multi-frame image super-resolution with fast upscaling technique[J/OL]. arXiv preprint arXiv: 1706.06266, 2017.

[10] [10] Rasti P, Demirel H, Anbarjafari G. Improved iterative back projection for video super-resolution[C]//2014 22nd Signal Processing and Com-munications Applications Conference (SIU). IEEE, 2014: 552-555.

[12] [12] Kato T, Hino H, Murata N. Multi-frame image super resolution based on sparse coding[J]. Neural Networks, 2015, 66: 64-78.

[13] [13] Greaves A, Winter H. Multi-frame video super-resolution using convolutional neural networks[J]. IEEE Transactions on Multimedia, 2017, 8(3):76-85.

[14] [14] Hayat K. Multimedia super-resolution via deep learning: a survey[J]. Digital Signal Processing, 2018, 81: 198-217.

[15] [15] DONG W, ZHANG L, Lukac R, et al. Sparse representation-based image interpolation with nonlocal autoregressive modeling[J]. IEEE Transactions on Image Processing, 2013, 22(4): 1382-1394.

[16] [16] WANG L, WU H, PAN C. Fast image upsampling via the displacement field[J]. IEEE Transactions on Image Processing, 2014, 23(12): 5123-5135.

[17] [17] ZHANG Y, FAN Q, BAO F, et al. Single-image super-resolution based on rational fractal interpolation[J]. IEEE Transactions on Image Processing, 2018, 27(8): 3782-3797.

[18] [18] Manj J V, Coupé P, Buades A, et al. Non-local MRI upsampling[J]. Medical Image Analysis, 2010, 14(6): 784-792.

[19] [19] ZHANG X, Burger M, Bresson X, et al. Bregmanized nonlocal regularization for deconvolution and sparse reconstruction[J]. SIAM Journal on Imaging Sciences, 2010, 3(3): 253-276.

[20] [20] LI L, XIE Y, HU W, et al. Single image super-resolution using combined total variation regularization by split Bregman Iteration[J]. Neurocomputing, 2014, 142: 551-560.

[21] [21] Rasti P, Demirel H, Anbarjafari G. Improved iterative back projection for video super-resolution[C]//2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014: 552-555.

[22] [22] YANG J, Wright J, HUANG T S, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873.

[23] [23] WANG X, YU K, DONG C, et al. Recovering realistic texture in image super-resolution by deep spatial feature transform[C]//Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition, 2018: 606-615.

[24] [24] Zoph B, Le Q V. Neural architecture search with reinforcement learning[J/OL]. arXiv preprint arXiv: 1611.01578, 2016.

[25] [25] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

[26] [26] Candes E, Romberg J. Sparsity and incoherence in compressive sampling[J]. Inverse Problems, 2007, 23(3): 969.

[27] [27] Sulam J, Ophir B, Zibulevsky M, et al. Trainlets: Dictionary learning in high dimensions[J/OL]. arXiv preprint arXiv: 1602.00212, 2016.

[28] [28] ZHANG H, Hager W W. A nonmonotone line search technique and its application to unconstrained optimization[J]. SIAM Journal on Opti-mization, 2004, 14(4): 1043-1056.

[29] [29] JIN J, YUAN J, SHEN Q, et al. Curvelet transform based adaptive image deblocking method[J]. Computers & Electrical Engineering, 2014, 40(8): 117-129.

[30] [30] Chengbo L. An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing[D]. Houston: Department of Computational and Applied Mathematics, 2009.