[1] C DONG, C C LOY, K M HE et al. Learning a Deep Convolutional Network for Image Super-Resolution, 184-199(2014).

[2] H KIM et al. Enhanced deep residual networks for single image super-resolution, 1132-1140(21).

[3] Y L ZHANG, K P LI, K LI et al. Image Super-Resolution Using Very Deep Residual Channel Attention Networks, 294-310(2018).

[4] W Z SHI, J CABALLERO, F HUSZÁR et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, 1874-1883(27).

[5] [5] 5蔡体健， 彭潇雨， 石亚鹏， 等. 通道注意力与残差级联的图像超分辨率重建［J］. 光学 精密工程， 2021， 29（1）： 142-151. doi: 10.37188/OPE.20212901.0142CAIT J， PENGX Y， SHIY P， et al. Channel attention and residual concatenation network for image super-resolution［J］. Opt. Precision Eng.， 2021， 29（1）： 142-151. （in Chinese）. doi: 10.37188/OPE.20212901.0142

[6] [6] 6程德强， 赵佳敏， 寇旗旗， 等. 多尺度密集特征融合的图像超分辨率重建［J］. 光学 精密工程， 2022， 30（20）： 2489-2500. doi: 10.37188/OPE.20223020.2489CHENGD Q， ZHAOJ M， KOUQ Q， et al. Multi-scale dense feature fusion network for image super-resolution［J］. Opt. Precision Eng.， 2022， 30（20）： 2489-2500. （in Chinese）. doi: 10.37188/OPE.20223020.2489

[7] [7] 7耿铭昆， 吴凡路， 王栋. 轻量化火星遥感影像超分辨率重建网络［J］. 光学 精密工程， 2022， 30（12）： 1487-1498. doi: 10.37188/OPE.20223012.1487GENGM K， WUF L， WANGD. Lightweight Mars remote sensing image super-resolution reconstruction network［J］. Opt. Precision Eng.， 2022， 30（12）： 1487-1498. （in Chinese）. doi: 10.37188/OPE.20223012.1487

[8] H Y LIU, Z B RUAN, P ZHAO et al. Video super-resolution based on deep learning： a comprehensive survey. Artificial Intelligence Review, 55, 5981-6035(2022).

[9] A KAPPELER, S YOO, Q Q DAI et al. Video super-resolution with convolutional neural networks. IEEE Transactions on Computational Imaging, 2, 109-122(2016).

[10] K C K CHAN, X T WANG, K YU et al. BasicVSR： the search for essential components in video super-resolution and beyond, 4945-4954(20).

[11] K C K CHAN, S C ZHOU, X Y XU et al. Basicvsr： improving video super-resolution with enhanced propagation and alignment, 5962-5971(18).

[12] X T WANG, K C K CHAN, K YU et al. EDVR： video restoration with enhanced deformable convolutional networks, 1954-1963(16).

[13] J CABALLERO, C LEDIG, A AITKEN et al. Real-time video super-resolution with spatio-temporal networks and motion compensation, 2848-2857(21).

[14] T F XUE, B A CHEN, J J WU et al. Video enhancement with task-oriented flow. International Journal of Computer Vision, 127, 1106-1125(2019).

[15] X TAO, H Y GAO, R J LIAO et al. Detail-revealing deep video super-resolution, 4482-4490(22).

[16] Q S YAN, D GONG, Q F SHI et al. Attention-guided network for ghost-free high dynamic range imaging, 1751-1760(15).

[17] O KUPYN, T MARTYNIUK, J R WU et al. DeblurGAN-v2： Deblurring （orders-of-magnitude） faster and better, 8877-8886.

[18] Y P TIAN, Y L ZHANG, Y FU et al. TDAN： Temporally-deformable alignment network for video super-resolution, 3357-3366(13).

[19] S W OH, J KANG et al. Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation, 3224-3232(18).

[20] S Y KIM, T NA et al. 3DSRnet： Video Super-Resolution Using 3D Convolutional Neural Networks. arXiv, 1812-09079(2018). https：//arxiv.org/abs/1812.09079

[21] S LI, F X HE, B DU et al. Fast spatio-temporal residual network for video super-resolution, 10514-10523(15).

[22] Y HUANG, W WANG, L WANG. Video super-resolution via bidirectional recurrent convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 1015-1028(2018).

[23] X B ZHU, Z Z LI, X Y ZHANG et al. Residual invertible spatio-temporal network for video super-resolution. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 5981-5988(2019).

[24] D FUOLI, S H GU, R TIMOFTE. Efficient video super-resolution through recurrent latent space propagation, 3476-3485(27).

[25] J M J VALANARASU, V M PATEL. UNeXt： MLP-Based Rapid Medical Image Segmentation Network. Lecture Notes in Computer Science, 23-33(2022).

[26] A RANJAN, M J BLACK. Optical flow estimation using a spatial pyramid network, 21, 4161-4170(2017).

[27] S W ZAMIR, A ARORA, S KHAN et al. Multi-stage progressive image restoration, 20, 14821-14831(2021).

[28] P YI, Z Y WANG, K JIANG et al. Omniscient video super-resolution, 10, 4429-4438(2021).

[29] Z Y WANG, P YI, K JIANG et al. Multi-memory convolutional neural network for video super-resolution. IEEE Transactions on Image Processing, 28, 2530-2544(2019).

[30] P YI, Z Y WANG, K JIANG et al. Multi-temporal ultra dense memory network for video super-resolution. IEEE Transactions on Circuits and Systems for Video Technology, 30, 2503-2516(2020).

[31] P YI, Z Y WANG, K JIANG et al. Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations, 3106-3115(2019).

[32] T ISOBE, X JIA, S H GU et al. Video Super-Resolution with Recurrent Structure-Detail Network, 645-660(2020).

[33] MS SAJJADI, R VEMULAPALLI, M BROWN. Frame-recurrent video super-resolution, 6626-6634(2018).

[34] B YAN, C LIN, W TAN. Frame and feature-context video super-resolution, 33, 5597-5604(2019).

[35] I LOSHCHILOV, F HUTTER. SGDR： Stochastic Gradient Descent with Warm Restarts. arXiv, 1608-03983(2016). https：//arxiv.org/abs/1608.03983

[36] D P KINGMA, J BA. Adam： a Method for Stochastic Optimization. arXiv, 1412-6980(2014). https：//arxiv.org/abs/1412.6980

[37] S BAIK, S HONG et al. NTIRE 2019 challenge on video deblurring and super-resolution： dataset and study, 1996-2005(16).

[38] C LIU, D Q SUN. On Bayesian adaptive video super resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 346-360(2014).

[39] W B LI, X TAO, T A GUO et al. MuCAN： Multi-Correspondence Aggregation Network For Video Super-Resolution, 335-351(2020).

[40] T ISOBE, S J LI, X JIA et al. Video super-resolution with temporal group attention, 8005-8014(13).

[41] T ISOBE, F ZHU, S WANG. Revisiting Temporal Modeling for Video Super-Resolution. arXiv, 2008-05765(2020). https：//arxiv.org/abs/2008.05765

[42] M HARIS, G SHAKHNAROVICH, N UKITA. Recurrent back-projection network for video super-resolution, 3892-3901(15).

[43] P YI, Z WANG, K JIANG et al. Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations, 3106-3115(2019).

[44] R YANG, S WANG, X WU et al. Using lightweight convolutional neural network to track vibration displacement in rotating body video. Mechanical Systems and Signal Processing, 177, 109137(2022).

[45] J W ZHOU, H G LI, L ZHANG et al. Vibration measurement with video processing based on alternating optimization of frequency and phase shifts. IEEE Transactions on Instrumentation and Measurement, 70, 1-13(2021).