Optics and Precision Engineering, Volume. 32, Issue 16, 2564(2024)

Lightweight video super-resolution based on hybrid spatio-temporal convolution

Zhenping XIA1,3、*, Hao CHEN1, Yuning ZHANG2,4, Cheng CHENG1,3, and Fuyuan HU1,3
Author Affiliations
  • 1School of Electronic & Information Engineering, Suzhou University of Science and Technology, Suzhou25009, China
  • 2Display R&D Centre, School of Electronic Science & Engineering, Southeast University, Nanjing10096, China
  • 3Jiangsu Industrial Intelligent and Low-carbon Technology Engineering Center, Suzhou215009, China
  • 4Shi-Cheng Laboratory for Information Display and Visualization, Nanjing210013, China
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    References(30)

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

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

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

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

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

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

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

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

    [12] [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)

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

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

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

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

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

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

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

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

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

    [23] 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, 13, 600-612(2004).

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

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

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

    [29] 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, 13, 600-612(2004).

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

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    Zhenping XIA, Hao CHEN, Yuning ZHANG, Cheng CHENG, Fuyuan HU. Lightweight video super-resolution based on hybrid spatio-temporal convolution[J]. Optics and Precision Engineering, 2024, 32(16): 2564

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    Paper Information

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    Received: Mar. 28, 2024

    Accepted: --

    Published Online: Nov. 18, 2024

    The Author Email: Zhenping XIA (xzp@usts.edu.cn)

    DOI:10.37188/OPE.20243216.2564

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