Spectroscopy and Spectral Analysis, Volume. 42, Issue 5, 1553(2022)

A Denoising Method Based on Back Propagation Neural Network for Raman Spectrum

Zhong WANG... Dong-dong WAN, Chuang SHAN, Yue-e LI and Qing-guo ZHOU*; |Show fewer author(s)
Author Affiliations
  • School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
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    Figures & Tables(11)
    Comparison of different denoising methods for 20 db noise
    Comparison of different denoising methods for 30 db noise
    RMSE and SNR changes of different wavelets and different thresholds for 20 db noise
    RMSE and SNR changes of different wavelets and different thresholds for 30 db noise
    RMSE and SNR changes of different wavelet decomposition levels for different noise
    RMSE and SNR changes of different denoising methods for 20 db noise
    RMSE and SNR changes of different denoising methods for 30 db noise
    Raman Spectrum of experimental sample(a): Noisy Raman spectrum; (b): Denoised spectrum by analyst; (c): Denoised spectrum by BP network
    • Table 1. Parameter settings of various methods

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      Table 1. Parameter settings of various methods

      滑动窗口平均值S-G滤波法小波阈值
      窗口大小窗口大小阶数tptrsorhscalnwname
      20 db15213heursuressln8sym7
      30 db30212sqtwologhmln6sym5
    • Table 2. Comparison of various methods for RMSE and SNR

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      Table 2. Comparison of various methods for RMSE and SNR

      原始含噪神经网络滑动窗口均值S-G滤波傅里叶变换小波阈值
      20 dbRMSE9.719 02.616 42.990 83.372 44.379 92.317 7
      SNR12.570 923.969 122.807 721.764 719.494 025.022 2
      30 dbRMSE31.605 36.772 78.127 811.054 513.855 86.183 7
      SNR2.328 115.708 214.123 911.452 59.490 716.498 4
    • Table 3. Comparison of various methods for RMSE and SNR on different Noise

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      Table 3. Comparison of various methods for RMSE and SNR on different Noise

      神经网络傅里叶变换小波阈值滑动窗口均值S-G滤波器
      RMSE¯2.787 94.362 62.434 47.745 63.912 1
      20 dbSNR¯22.950 818.819 324.031 616.958 019.989 8
      S99219699
      RMSE¯7.197 413.460 86.692 811.268 410.688 7
      30 dbSNR¯14.565 09.020 315.285 011.543 011.043 6
      S1002793100
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    Zhong WANG, Dong-dong WAN, Chuang SHAN, Yue-e LI, Qing-guo ZHOU. A Denoising Method Based on Back Propagation Neural Network for Raman Spectrum[J]. Spectroscopy and Spectral Analysis, 2022, 42(5): 1553

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    Paper Information

    Category: Research Articles

    Received: Apr. 14, 2021

    Accepted: --

    Published Online: Nov. 10, 2022

    The Author Email:

    DOI:10.3964/j.issn.1000-0593(2022)05-1553-08

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