[2] [2] 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.

[3] [3] 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, Honolulu, HI, 2017: 105–114.

[5] [5] Nasrollahi K, Moeslund T B. Super-resolution: a comprehen-sive survey[J]. Machine Vision and Applications, 2014, 25(6): 1423–1468.

[6] [6] Yue LW, ShenH F, LiJ, et al. Image super-resolution: the techniques, applications, and future[J]. Signal Processing, 2016, 128: 389–408.

[7] [7] Tsai R Y, Huang T S. Multi-frame image restoration and regis-tration[M]//Huang T S. Advances in Computer Vision and Im-age Processing. Greenwich: JAI Press, 1984: 317–339.

[8] [8] Shen H F, Peng L, Yue L W, et al. Adaptive norm selection for regularized image restoration and super-resolution[J]. IEEE Transactions on Cybernetics, 2016, 46(6): 1388–1399.

[9] [9] Zeng X Y, Yang L H. A robust multiframe super-resolution algo-rithm based on half-quadratic estimation with modified BTV regularization[J]. Digital Signal Processing, 2013, 23(1): 98–109.

[10] [10] Huang S Y, Sun J , Yang Y, et al. Multi-frame super-resolution reconstruction based on gradient vector flow hybrid field[J]. IEEE Access, 2017, 5: 21669–21683.

[11] [11] Molina R, Vega M, Abad J, et al. Parameter estimation in Bayesian high-resolution image reconstruction with multisen-sors[J]. IEEE Transactions on Image Processing, 2003, 12(12): 1655–1667.

[12] [12] Babacan S D, Molina R, Katsaggelos A K. Variational Bayesian super resolution[J]. IEEE Transactions on Image Processing, 2011, 20(4): 984–999.

[13] [13] Villena S, Vega M, Molina R, et al. A non-stationary image prior combination in super-resolution[J]. Digital Signal Processing, 2014, 32: 1–10.

[14] [14] Liu C, Sun D Q. On Bayesian adaptive video super resolution[J]. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 2014, 36(2): 346–360.

[15] [15] Ng M K, Shen H F, Lam E Y, et al. A total variation regulariza-tion based super-resolution reconstruction algorithm for digital video[J]. EURASIP Journal on Advances in Signal Processing, 2007, 2007: 074585.

[16] [16] Beal M J. Variational algorithms for approximate Bayesian inference[D]. London, UK: University College London, 2003.

[17] [17] Bioucas-Dias J M, Figueiredo M A T, Oliveira J P. Total varia-tion-based image deconvolution: a majorization-minimization approach[C]//Proceedings of 2006 IEEE International Confe-rence on Acoustics Speech and Signal Processing Proceed-ings, Toulouse, 2006: 861–864.

[18] [18] Dong C, Loy C C, He K M, et al. Image super-resolution using deep convolutional networks[EB/OL]. http://mmlab.ie.cuhk. edu.hk /projects/SRCNN.html.

[19] [19] Lucas B, Kanade T. An iterative image registration technique with an application to stereo vision[C]//Proceedings of the 7th International Joint Conference on Artificial Intelligence, San Francisco, 1981: 674–679.

[20] [20] 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, 2009, 26(1): 98–117.

[21] [21] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assess-ment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600–612.