[1] 林毅，周芃，陈彦明. 基于语义注意力的医学图像超分辨率方法［J］. 计算机科学， 2023， 50（S2）： 1017-1022.LINY， ZHOUP， CHENY M. Medical image super-resolution method based on semantic attention［J］. Computer Science， 2023， 50（S2）： 1017-1022. （in Chinese）

[2] M A KHAN, K JAVED, SALI KHAN et al. Human action recognition using fusion of multiview and deep features： an application to video surveillance. Multimedia Tools and Applications, 83, 14885-14911(2024).

[3] 易见兵， 陈俊宽， 曹锋， 等. 轻量级重参数化的遥感图像超分辨率重建网络设计［J］. 光学 精密工程， 2024， 32（2）： 268-285.YIJ B， CHENJ K， CAOF， et al. Design of lightweight re-parameterized remote sensing image super-resolution network［J］. Opt. Precision Eng.， 2024， 32（2）： 268-285.（in Chinese）

[4] L G LUO, B S YI, Z Y WANG et al. Efficient lightweight network for video super-resolution. Neural Computing and Applications, 36, 883-896(2024).

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

[6] J KIM, J K LEE, K M LEE. Accurate Image Super-Resolution Using Very Deep Convolutional Networks, 1646-1654(2016).

[7] 周颖， 裴盛虎， 陈海永， 等. 基于多尺度自适应注意力的图像超分辨率网络［J］. 光学 精密工程， 2024， 32（6）： 843-856.ZHOUY， PEIS H， CHENH Y， et al. Image super-resolution network based on multi-scale adaptive attention［J］. Opt. Precision Eng.， 2024， 32（6）： 843-856.（in Chinese）

[8] J LIU, J TANG, G S WU. Residual Feature Distillation Network for Lightweight Image Super-Resolution, 41-55(2020).

[9] M YASIR, I ULLAH, C CHOI. Depthwise channel attention network （DWCAN）： an efficient and lightweight model for single image super-resolution and metaverse gaming. Expert Systems, 41(2024).

[10] X SUN, X LONG, D L HE et al. VSRNet： end-to-end video segment retrieval with text query. Pattern Recognition, 119, 108027(2021).

[11] Y HUANG, W WANG, L WANG. Bidirectional recurrent convolutional networks for multi-frame super-resolution. Advances in Neural Information Processing Systems, 28(2015).

[12] 王森， 祝阳， 张印辉， 等. 多阶段帧对齐的视频超分辨率重建网络［J］. 光学 精密工程， 2023， 31（16）： 2430-2443.WANGS， ZHUY， ZHANGY H， et al. Multi-stage frame alignment video super-resolution network［J］. Opt. Precision Eng.， 2023， 31（16）： 2430-2443.（in Chinese）

[13] S Y KIM, T NA et al. 3dsrnet： Video super-resolution using 3d convolutional neural networks. https：//arxiv.org/pdf/1812.09079.pdf

[14] S W OH, J KANG et al. Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation, 3224-3232(2018).

[15] X T WANG, K C K CHAN, K YU et al. EDVR： Video Restoration with Enhanced Deformable Convolutional Networks, 1954-1963(2019).

[16] Q S YAN, D GONG, J Q SHI et al. Dual-attention-guided network for ghost-free high dynamic range imaging. International Journal of Computer Vision, 130, 76-94(2022).

[17] D GONG, L Q LIU, V LE et al. Memorizing Normality to Detect Anomaly： Memory-Augmented Deep Autoencoder For Unsupervised Anomaly Detection, 1705-1714(2019).

[18] 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).

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

[20] X TAO, H Y GAO, R J LIAO et al. Detail-revealing deep video super-resolution, 4482-4490(2017).

[21] Y P TIAN, Y L ZHANG, Y FU et al. TDAN： temporally-deformable alignment network for video super-resolution, 3357-3366(2020).

[22] X B GAO, W LU, D C TAO et al. Image quality assessment based on multiscale geometric analysis. IEEE Transactions on Image Processing： a Publication of the IEEE Signal Processing Society, 18, 1409-1423(2009).

[23] Z WANG, A C BOVIK, H R SHEIKH et al. Image quality assessment： from error visibility to structural similarity. IEEE Transactions on Image Processing, 13, 600-612(2004).

[24] D P KINGMA, J BA. Adam： A method for stochastic optimization. https：//arxiv.53yu.com/pdf/1412.6980.pdf

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

[26] J Z CAO, Y W LI, K ZHANG et al. Video super resolution transformer. https：//arxiv.org/pdf/2106.06847.pdf

[27] K C K CHAN, S C ZHOU, X Y XU et al. BasicVSR： improving video super-resolution with enhanced propagation and alignment, 5962-5971(2022).

[28] R J LIAO, X TAO, R Y LI et al. Video super-resolution via deep draft-ensemble learning, 531-539(2015).

[29] Z WANG, A C BOVIK, H R SHEIKH et al. Image quality assessment： from error visibility to structural similarity. IEEE Transactions on Image Processing, 13, 600-612(2004).

[30] L X LIU, B LIU, H HUANG et al. No-reference image quality assessment based on spatial and spectral entropies. Signal Processing： Image Communication, 29, 856-863(2014).