Volume 30 Issue 3
May  2021
Turn off MathJax
Article Contents
HUANG Jinwang, LYU Shanxiang, CHEN Yue. Chaotic Signal Denoising Algorithm Based on Self-Similarity[J]. Chinese Journal of Electronics, 2021, 30(3): 482-488. doi: 10.1049/cje.2021.04.001
Citation: HUANG Jinwang, LYU Shanxiang, CHEN Yue. Chaotic Signal Denoising Algorithm Based on Self-Similarity[J]. Chinese Journal of Electronics, 2021, 30(3): 482-488. doi: 10.1049/cje.2021.04.001

Chaotic Signal Denoising Algorithm Based on Self-Similarity

doi: 10.1049/cje.2021.04.001

This work is supported by the National Natural Science Foundation of China (No.61872083, No.61902149, No.61932010, No.62032009), the Natural Science Foundation of Guangdong Province (No.2017A030310659, No.2019A1515011123), and the Key Scientific Research Platforms and Projects of Universities in Guangdong Province (No.2018KTSCX344).

  • Received Date: 2019-07-01
  • Inspired by the self-similar fractal properties of chaotic attractors and the heuristics of similarity filtering of images, a novel chaotic signal denoising algorithm is proposed. By grouping the chaotic signal with similar segments, the denoising of one-dimensional input is transformed into a two-dimensional joint filtering problem. Singular value decomposition is performed on the grouped signal segments and the transform coefficients are processed by thresholding to attenuate noise and finally undergo inverse transformation to recover the signal. Because the similar segments in the grouping have good correlation, the two-dimensional transformation of the grouping can obtain a more sparse representation of the original signal compared with the threshold value denoising in the direct one-dimensional transform domain, thereby having better noise suppression effect. Simulation results show that the algorithm can improve the reconstruction accuracy and has better signal-to-noise ratio than existing chaotic signal denoising algorithms.
  • loading
  • J. Cai, Y. Li, W. Li, et al., “Two entropy-based criteria design for signal complexity measures”, Chinese Journal of Electronics, Vol.28, No.6, pp.1139–1143, 2019.
    L. Zhang, Y. Ji and M. Luo, “Parameter estimation of weak signal based on the steady attractor of duffing oscillator”, Chinese Journal of Electronics, Vol.28, No.4, pp.781–788, 2019.
    X. Liu, S. Qiu and F. Lau, “Deterministic approaches for noncoherent communications with chaotic carriers”, Journal of Systems Engineering and Electronics, Vol.16, No.2, pp.253–257, 2005.
    K. Pukenas, “Algorithm for noise reduction for strongly contaminated chaotic oscillators based on the local projection approach and 2D wavelet filtering”, Journal of Vibroengineering, Vol.18, No.4, pp.2537–2544, 2016.
    W. Wang, Y. Jin, B. Wang, et al., “Chaotic signal de-noising based on adaptive threshold synchrosqueezed wavelet transform”, Acta Electronica Sinica, Vol.46, No.7, pp.1652–1657, 2018.
    W. Dong, H. Ding, X. Dong, et al., “An adaptive wavelet threshold de-nosing both in low and high frequency domains”, Chinese Journal of Electronics, Vol.43, No.12, pp.2374–2380, 2015.
    K. Yannis and M. Stephen, “Development of EMD-based denoising methods inspired by wavelet thresholding”, IEEE Transactions on Signal Processing, Vol.57, No.4, pp.1351–1362, 2009.
    G. Li and S. Lyu, “Chaotic signal denoising in a compressed sensing perspective”, Acta Physica Sinica, Vol.64, No.16, pp.160502, 2015.
    J. Gao, H. Sultan, J. Hu, et al., “Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: A comparison”, IEEE Signal Processing Letters, Vol.17, No.3, pp.237–240, 2010.
    M. Wang, Z. Wu and J. Feng, “A parameter optimization nonlinear adaptive denoising algorithm for chaotic signals”, Acta Physica Sinica, Vol.64, No.4, pp.40503, 2015.
    V. Fedorov and C. Ballester, “Affine non-local means image denoising”, IEEE Transactions on Image Processing, Vol.26, No.5, pp.2137–2148, 2017.
    D. Kostadin, F. Alessandro, K. Vladimir and E. Karen, “Image denoising by sparse 3-D transform-domain collaborative filtering”, IEEE Transactions on Image Processing, vol.16, No.8, pp.2080–2095, 2007.
    G. Chen, G. Luo, L. Tian, et al., “Noise reduction for images with non-uniform noise using adaptive block matching 3D filtering”, Chinese Journal of Electronics, Vol.26, No.6, pp.1227–1232, 2017.
    Q. Guo, C. Zhang, Y. Zhang, et al., “An efficient SVD-based method for image denoising”, IEEE Transactions on Circuits and Systems for Video Technology, Vol.26, No.5, pp.868–880, 2016.
    S. M. Yu, Chaotic Systems and Chaotic Circuits:Principle, Design and Its Application in Communications, Xidian University Press, Xi’an, China, pp.10–12, 2011.
    H. Tao and Z. Zhou, “Prediction of chaotic time series based on fractal self-affinity”, Acta Physica Sinica, Vol.56, No.2, pp.693–700, 2007.
    G. Golub and C. Van Loan, Matrix Computations, Johns Hopkins University Press, Baltimore, MD, USA, 2013.
    B. D. Moor, “The singular value decomposition and long and short spaces of noisy matrices”, IEEE Transactions on Signal Processing, Vol.41, No.9, pp.2826–2838, 1993.
    Y. Hou, C. Zhao, D. Yang, et al., “Comments on image denoising by sparse 3D transform domain collaborative filtering”, IEEE Transactions on Image Processing, Vol.20, No.1, pp.268–270, 2011.
    L. D. David and M. J. Iain, “Ideal spatial adaptation by wavelet shrinkage”, Biometrika, Vol.81, No.3, pp.425–455, 1994.
    D. James and W. Kahan, “Accurate singular values of bidiagonal matrices”, SIAM Journal on Scientific and Statistical Computing, Vol.11, No.5, pp.873–912, 1990.
    A. Rajwade, A. Rangarajan and A. Banerjee, “Image denoising using the higher order singular value decomposition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.35, No.3, pp.849–862, 2013.
    S. Lü, Z. Wang, Z. Hu, et al., “Gradient method for blind chaotic signal separation based on proliferation exponent”, 2014 Chinese Physics B, Vol.23, No.1, pp.142–147, 2013.
    K. Holger and S. Thomas,Nonlinear Time Series Analysis, Cambridge University Press, Cambridge, UK, pp.65–74, 2004.
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (114) PDF downloads(26) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint