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基于图谱域移位的带限图信号重构算法

杨杰 赵磊 郭文彬

杨杰, 赵磊, 郭文彬. 基于图谱域移位的带限图信号重构算法. 自动化学报, 2021, 47(9): 2132−2142 doi: 10.16383/j.aas.c200802
引用本文: 杨杰, 赵磊, 郭文彬. 基于图谱域移位的带限图信号重构算法. 自动化学报, 2021, 47(9): 2132−2142 doi: 10.16383/j.aas.c200802
Yang Jie, Zhao Lei, Guo Wen-Bin. Graph band-limited signals reconstruction method based graph spectral domain shifting. Acta Automatica Sinica, 2021, 47(9): 2132−2142 doi: 10.16383/j.aas.c200802
Citation: Yang Jie, Zhao Lei, Guo Wen-Bin. Graph band-limited signals reconstruction method based graph spectral domain shifting. Acta Automatica Sinica, 2021, 47(9): 2132−2142 doi: 10.16383/j.aas.c200802

基于图谱域移位的带限图信号重构算法

doi: 10.16383/j.aas.c200802
基金项目: 国家自然科学基金(61271181) 资助
详细信息
    作者简介:

    杨杰:北京邮电大学信息与通信工程学院博士研究生. 主要研究方向为图信号处理. E-mail: yjie934@bupt.edu.cn

    赵磊:北京邮电大学信息与通信工程学院博士研究生. 主要研究方向为无线信号处理. E-mail: leizhao@bupt.edu.cn

    郭文彬:北京邮电大学信息与通信工程学院教授. 主要研究方向为无线信号处理. 本文通信作者. E-mail: gwb@bupt.edu.cn

Graph Band-limited Signals Reconstruction Method Based Graph Spectral Domain Shifting

Funds: Supported by National Natural Science Foundation of China (61271181)
More Information
    Author Bio:

    YANG Jie Ph.D. candidate at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. His main research interest is graph signal processing

    ZHAO Lei Ph.D. candidate at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. His main research interest is wireless signal processing

    GUO Wen-Bin Professor at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. His main research interest is wireless signal processing. Corresponding author of this paper

  • 摘要: 针对带限图信号的重构问题, 本文提出了基于图谱域移位的带限图信号重构模型, 该模型将图带限分量的恒等不变特性建模为最小二乘问题. 基于所提出的重构模型, 本文设计了基于谱移位的重构算法和基于残差谱移位的重构算法. 相比于其他重构算法, 两种新算法提升了迭代效率和重构精度. 此外, 本文算法还适用于分段带限图信号的重构问题, 并且具有良好的迭代效率和重构精度.通过实验仿真表明, 相比于目前其他的带限图信号重构算法, 新算法的迭代效率提升约70%和重构精度提升约60%.
  • 图  1  带限图信号

    Fig.  1  Graph band-limited signals

    图  2  分段带限图信号

    Fig.  2  Graph sperate band-limited signals

    图  3  图信号采样

    Fig.  3  Graph signals sampling

    图  4  无噪环境下带限图信号重构性能对比

    Fig.  4  Comparison of graph band-limited signals reconstruction performances in noiseless environment

    图  5  含噪环境下带限图信号重构性能对比

    Fig.  5  Comparison of graph band-limited signals reconstruction performances in noisy environment

    图  6  分段带限图信号重构性能对比

    Fig.  6  Comparison of graph separate band-limited signals reconstruction performances

    表  1  无噪情况下基于随机采样的${G_1}$重构效率

    Table  1  ${G_1}$ reconstruction efficiency of random sampling in noiseless

    算法迭代次数运行时间 (s)
    ILSR 220 139.99
    OPGIR 114 108.78
    IPR 96 61.87
    IGDR 33 20.47
    BGSR-GFS 27 5.73
    BGSR-GFS-R 8 8.97
    下载: 导出CSV

    表  2  无噪情况下基于随机采样的${G_2}$重构效率

    Table  2  ${G_2}$ reconstruction efficiency of random sampling in noiseless

    算法迭代次数运行时间 (s)
    ILSR 269 0.1509
    OPGIR 139 0.1291
    IPR 64 0.0405
    IGDR 34 0.0271
    BGSR-GFS 7 0.0065
    BGSR-GFS-R 5 0.0146
    下载: 导出CSV
  • [1] 刘建伟, 黎海恩, 罗雄麟. 概率图模型学习技术研究进展[J]. 自动化学报, 2014, 40(06): 1025-1044.

    Liu Jian-Wei, Li Hai-En, Luo Xiong-Lin. Learning technique of probabilistic graphical models: a review. Acta Automatica Sinica, 2014, 40(6): 1025−1044.
    [2] Shuman D I, Narang S K, Frossard P, Ortega A, Vandergheynst P. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine, 2013, 30(3): 83-98 doi: 10.1109/MSP.2012.2235192
    [3] Tremblay N, Gonalves P, Borgnat P. Design of graph filters and filterbanks. Cooperative and Graph Signal Processing. Academic Press, 2018: 299−324
    [4] Chen S H, Varma R, Sandryhaila A, Kovačević J. Discrete signal processing on graphs: Sampling theory. IEEE Transactions on Signal Processing, 2015, 63(24): 6510-6523 doi: 10.1109/TSP.2015.2469645
    [5] Hu W, Cheung G, Ortega A, Au O C. Multiresolution graph Fourier transform for compression of piecewise smooth images. IEEE Transactions on Image Processing, 2014, 24(1): 419-433
    [6] Liu Y L, Yang L S, You K Y, Guo W B, Wang W B. Graph learning based on spatiotemporal smoothness for time-varying graph signal. IEEE Access, 2019, 7: 62372-62386 doi: 10.1109/ACCESS.2019.2916567
    [7] 蒋俊正, 杨杰, 欧阳缮. 一种新的无线传感器网络中异常节点检测定位算法[J]. 电子与信息学报, 2018, 40(10): 2358-2364.

    Jiang Jun-Zheng, Yang Jie, Ouyang Shan. Novel method for outlier nodes detection and localization in wireless sensor networks. Journal of Electronics and Information Technology, 2018, 40(10): 2358-2364.
    [8] 杨杰, 蒋俊正. 利用联合图模型的传感器网络数据修复方法[J]. 西安电子科技大学学报, 2020, 47(01): 44-51.

    YANG Jie, JIANG Junzheng. Method for data recovery in the sensor network based on the joint graph model[J]. Journal of Xidian University, 2020, 47(1): 44-51.
    [9] 廖祥文, 陈兴俊, 魏晶晶, 陈国龙, 程学旗. 基于多层关系图模型的中文评价对象与评价词抽取方法[J]. 自动化学报, 2017, 43(03): 462-471.

    LIAO Xiang-Wen, CHEN Xing-Jun, WEI Jing-Jing, CHEN Guo-Long, CHENG Xue-Qi. A Multi-layer Relation Graph Model for Extracting Opinion Targets and Opinion Words. Acta Automatica Sinica, 2017, 43(3): 462-471.
    [10] 张建朋, 裴雨龙, 刘聪, 李邵梅, 陈鸿昶. 基于因子图模型的动态图半监督聚类算法[J]. 自动化学报, 2020, 46(04): 670-680.

    ZHANG Jian-Peng, PEI Yu-Long, LIU Cong, LI Shao-Mei, CHEN Hong-Chang. A Semi-supervised Clustering Algorithm Based on Factor Graph Model for Dynamic Graphs. Acta Automatica Sinica, 2020, 46(4): 670-680.
    [11] Ortega A, Frossard P, Kovačević J, Moura J M F, Vandergheyns P. Graph signal processing: Overview, challenges, and applications. Proceedings of the IEEE, 2018, 106(5): 808-828. doi: 10.1109/JPROC.2018.2820126
    [12] Tanaka Y. Spectral domain sampling of graph signals[J]. IEEE Transactions on Signal Processing, 2018, 66(14): 3752-3767. doi: 10.1109/TSP.2018.2839620
    [13] Guler B, Avestimehr S, Ortega A. TACC: Topology-Aware Coded Computing for Distributed Graph Processing[J]. IEEE Transactions on Signal and Information Processing over Networks, 2020, 6(99): 508-525.
    [14] Hosseinalipour S, Wang J, Dai H Y, Wang W Y. Detection of infections using graph signal processing in heterogeneous networks. In: Proceedings of the 2017 IEEE Global Communications Conference. Singapore, Singapore: IEEE, 2017.1−6.
    [15] Shuman D I, Faraji M J, Vandergheynst P. A multiscale pyramid transform for graph signals[J]. IEEE Transactions on Signal Processing, 2015, 64(8): 2119-2134.
    [16] Shen Y, Baingana B, Giannakis G B. Tensor decompositions for identifying directed graph topologies and tracking dynamic networks[J]. IEEE Transactions on Signal Processing, 2017, 65(14): 3675-3687. doi: 10.1109/TSP.2017.2698369
    [17] Tanaka Y, Eldar Y C, Ortega A, Cheung G. Sampling signals on graphs: From theory to applications. IEEE Signal Processing Magazine, 2020, 37(6): 14-30 doi: 10.1109/MSP.2020.3016908
    [18] Ferreira P. Interpolation and the Discrete Papoulis Gerchberg Algorithm[J]. IEEE Transactions on Signal Processing, 1994, 42(10): 2596-2606. doi: 10.1109/78.324726
    [19] Narang S K, Gadde A, Sanou E, Ortega A. Localized iterative methods for interpolation in graph structured data. In: Proceedings of the 2013 IEEE Global Conference on Signal and Information Processing. Austin, TX, USA: IEEE, 2013. 491−494
    [20] Wang X, Liu P, Gu Y. Local-set-based graph signal reconstruction[J]. IEEE Transactions on Signal Processing, 2015, 63(9): 2432-2444. doi: 10.1109/TSP.2015.2411217
    [21] Yang L S, You K Y, Guo W B. Bandlimited graph signal reconstruction by diffusion operator. EURASIP Journal on Advances in Signal Processing, 2016, Article number: 120 (2016)
    [22] Brugnoli E, Toscano E, Vetro C. Iterative Reconstruction of Signals on Graph[J]. IEEE Signal Processing Letters, 2019, 27: 76-80.
    [23] Tseng C C, Lee S L. A missing data recovery method of sparse graph signal in GFT domain. In: Proceedings of the 2018 IEEE International Conference on Consumer Electronics. Taipei, China: IEEE, 2018. 1−2
    [24] Tseng C C, Lee S L, Su R H. A missing temperature data estimation method using graph Fourier transform. In: Proceedings of the 2017 IEEE International Conference on Consumer Electronics. Taipei, China: IEEE, 2017. 87−88
    [25] Sandryhaila A, Moura J M F. Discrete signal processing on graphs: Graph Fourier transform. In: Proceedings of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing. Vancouver, BC, Canada: IEEE, 2013. 6167−6170.
    [26] Narang S K, Gadde A, Ortega A. Signal processing techniques for interpolation in graph structured data. In: Proceedings of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing. Vancouver, BC, Canada: IEEE, 2013. 5445−5449.
    [27] Chen S H, Varma R, Singh A, Kovačević J. Representations of piecewise smooth signals on graphs. In: Proceedings of the 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing. Shanghai, China: IEEE, 2016. 6370−6374
    [28] Youla D C, Webb H. Image restoration by the method of convex projections: Part I-Theory[J]. IEEE Transactions on Medical Imaging, 1982, 1(2), 81–94. doi: 10.1109/TMI.1982.4307555
    [29] Narang S K, Ortega A. Perfect reconstruction two-channel wavelet filter banks for graph structured data[J]. IEEE Transactions on Signal Processing, 2012, 60(6): 2786-2799. doi: 10.1109/TSP.2012.2188718
    [30] Sandryhaila A, Moura J M F. Discrete signal processing on graphs[J]. IEEE Transactions on Signal Processing, 2013, 61(7): 1644-1656. doi: 10.1109/TSP.2013.2238935
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出版历程
  • 收稿日期:  2020-09-28
  • 录用日期:  2021-01-26
  • 网络出版日期:  2021-04-28
  • 刊出日期:  2021-10-13

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