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基于层次特征复用的视频超分辨率重建

周圆 王明非 杜晓婷 陈艳芳

周圆, 王明非, 杜晓婷, 陈艳芳. 基于层次特征复用的视频超分辨率重建. 自动化学报, 2021, x(x): 1−11 doi: 10.16383/j.aas.c210095
引用本文: 周圆, 王明非, 杜晓婷, 陈艳芳. 基于层次特征复用的视频超分辨率重建. 自动化学报, 2021, x(x): 1−11 doi: 10.16383/j.aas.c210095
Zhou Yuan, Wang Ming-Fei, Du Xiao-Ting, Chen Yan-Fang. Video super-resolution via hierarchical feature reuse. Acta Automatica Sinica, 2021, x(x): 1−11 doi: 10.16383/j.aas.c210095
Citation: Zhou Yuan, Wang Ming-Fei, Du Xiao-Ting, Chen Yan-Fang. Video super-resolution via hierarchical feature reuse. Acta Automatica Sinica, 2021, x(x): 1−11 doi: 10.16383/j.aas.c210095

基于层次特征复用的视频超分辨率重建

doi: 10.16383/j.aas.c210095
基金项目: 国家自然科学基金联合基金项目(U2006211)资助, 国家重点研发计划项目(课题编号2020YFC1523204)资助
详细信息
    作者简介:

    周圆:天津大学电气自动化与信息工程学院副教授. 主要研究方向为计算机视觉与图像/视频通信. 本文通信作者. E-mail: zhouyuan@tju.edu.cn

    王明非:天津大学电气自动化与信息工程学院研究生. 主要研究方向为计算机视觉与机器学习. E-mail:

    杜晓婷:天津大学电气自动化与信息工程学院研究生. 主要研究方向为计算机视觉与机器学习. E-mail:

    陈艳芳:天津大学电气自动化与信息工程学院博士研究生. 主要研究方向为计算机视觉与机器学习. E-mail:

Video Super-Resolution via Hierarchical Feature Reuse

Funds: Supported by the National Natural Science Foundation of China (U2006211) and National Key Research and Development Program (2020YFC1523204)
More Information
    Author Bio:

    ZHOU Yuan Associate professor at the School of Electrical and Information Engineering, Tianjin University. Her research interests include computer vision and image/video communication. Corresponding author of the paper

    WANG Ming-Fei Master student at the School of Electrical and Information Engineering, Tianjin University. His research interests include computer vision and machine learning

    DU Xiao-Ting Master student at the School of Electrical and Information Engineering, Tianjin University. Her research interests include computer vision and machine learning

    CHEN Yan-Fang Ph.D. student at the School of Electrical and Information Engineering, Tianjin University. Her research interests include computer vision and machine learning

  • 摘要: 当前的深度卷积神经网络方法, 在视频超分辨率任务上实现的性能提升相对于图像超分辨率任务略低一些, 部分原因是它们对层次结构特征中的某些关键帧间信息的利用不够充分. 为此, 本文提出了一个称作层次特征复用网络(Hierarchical feature reuse network, HFRNet)的结构, 用以解决上述问题. 该网络保留运动补偿帧的低频内容, 并采用密集层次特征块(Dense hierarchical feature block, DHFB)自适应地融合其内部每个残差块的特征, 之后用长距离特征复用融合多个DHFB间的特征, 从而促进高频细节信息的恢复. 实验结果表明, 本文提出的方法在定量和定性指标上均优于当前的方法.
  • 图  1  层次特征复用网络(HFRNet)的结构, 上中下分别为整体结构、HFRNet(a)的特征融合模块与HFRNet (b)的特征融合模块

    Fig.  1  Architecture of Hierarchical Feature Reuse Network (HFRNet). Above: Overall Architecture; Middle: Feature Fusion Module of HFRNet(a), Below: Feature Fusion Module of HFRNet(b)

    图  2  DHFB的详细结构

    Fig.  2  Detailed architecture of Dense Hierarchical Feature Block (DHFB)

    图  3  本文方法和其他方法在VIDEO4和Myanmar数据集下得到的平均PSNR(dB)和平均SSIM

    Fig.  3  Average PSNR(dB) and SSIM for VIDEO4 and Myanmar dataset, between our method and other methods

    图  4  VIDEO4数据集下本文方法与其他方法的主观对比

    Fig.  4  Qualitative super-resolution comparison of HFRNet with other models on an image from VIDEO4 dataset.

    图  5  Myanmar数据集下本文方法与其他方法的主观对比

    Fig.  5  Qualitative super-resolution comparison of HFRNet with other models on an image from Myanmar dataset.

    图  6  本文方法重建结果与其他方法的细节对比

    Fig.  6  Qualitative super-resolution comparison of the reconstruction details by HFRNet and other models.

    表  1  不同DHFB数目(D)和每个DHFB残差块数目(R)对2倍率超分辨率重建性能的影响(PSNR(dB))

    Table  1  Average PSNR(dB) for 2x video super resolution task, with different number of DHFBs (D) and residual blocks (R) per DHFB

    模块组合方式CITY序列 (dB)WALK序列 (dB)FOLIAGE序列 (dB)CALENDAR序列 (dB)平均PSNR (dB)
    R4D634.34236.84632.04527.07132.576
    R6D434.33937.10132.11727.06732.656
    R6D634.89637.21032.22427.13732.866
    R6D834.901(±0.035)37.102(±0.054)32.187(±0.069)27.140(±0.007)32.833(±0.041)
    R8D634.633(±0.039)36.873(±0.025)32.144(±0.050)27.109(±0.019)32.690(±0.034)
    下载: 导出CSV

    表  2  不同网络结构实验结果的平均PSNR(dB)及所需参数量

    Table  2  Number of parameters with different structures of HFRNet, and average PSNR(dB) achieved in super-resolution task

    尺度网络结构参数量CITY序列 (dB)WALK序列 (dB)FOLIAGE序列 (dB)CALENDAR序列 (dB)平均PSNR (dB)
    x2无层次特征复用2.85M33.79335.91931.88426.29131.972
    HFRNet(a)3.01M34.89637.21032.22427.13732.866
    HFRNet(b)3.10M35.10437.21832.23027.15832.927
    x3无层次特征复用2.85M27.22030.11327.01923.34426.924
    HFRNet(a)3.01M28.23531.51327.53924.19027.869
    HFRNet(b)3.10M28.24031.61327.58724.21727.914
    下载: 导出CSV

    表  3  不同光流估计方法对超分辨率重建性能的影响(PSNR(dB))

    Table  3  Average PSNR(dB) in video super resolution task, with different optical flow estimation algorithm

    尺度光流估计算法CITY序列 (dB)WALK序列 (dB)FOLIAGE序列 (dB)CALENDAR序列 (dB)平均PSNR (dB)
    x2CNN-based35.22637.10632.24427.81733.098
    CLG-TV35.10437.21832.23027.15832.927
    x3CNN-based28.25532.10327.59024.76628.179
    CLG-TV28.24031.61327.58724.21727.914
    下载: 导出CSV

    表  4  不同运动补偿算法对超分辨率重建性能的影响(平均PSNR(dB))

    Table  4  Average PSNR(dB) in video super resolution task, with different motion compensation algorithm.

    运动补偿算法与参数尺度MC (k=0.050)MC (k=0.100)MC (k=0.125)MC (k=0.175)AMC
    平均PSNR (dB)x232.49332.51032.71432.61532.927
    x327.50527.68427.82227.69427.914
    下载: 导出CSV
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