Chinese Optics, Volume. 18, Issue 4, 794(2025)

A spectrum signal pre-processing algorithm based on multi-scale wavelet transform

Fang QIAN1、*, Yong-bo XU2, and Wei ZHAO3
Author Affiliations
  • 1China Academy of Electronics and Information Technology, Beijing 100041, China
  • 2Suzhou Everbright Photonics Co., Ltd., Suzhou 215100, China
  • 3Xichang Satellite Launch Center, Xichang 615000, China
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    Figures & Tables(18)
    Principle diagram of wavelet de-noising
    Flow chart of wavelet de-noising
    Flow chart of spike removal algorithm
    Flow chart of baseline correction
    Comparison between spectral signal and wavelet signal. (a) Single spectrum peak; (b) mult-spectrum peaks
    Comparison of denoising effects of simulated spectral signals
    Contrast map of simulated de-noising spectral signal. (a) Initial signal; (b) signal with noise; (c) hard thresholding denoising; (d) soft thresholding denoising; (e) Savitzky-Golay denoising; (f) DTD denoising
    Map of spectral signal with baseline. (a) Initial signal; (b) signal with linear baseline; (c) signal with Gaussian baseline; (d) signal with polynomial baseline; (e) signal with exponential baseline; (f) signal with sigmoidal baseline
    inVia confocal micro Raman spectrometer
    Principle diagram of spectral imaging
    Sample slide
    Comparison of denoising results for measured data. (a) Initial signal; (b) hard thresholding denoising; (c) soft thresholding denoising; (d) Savitzky-Golay denoising; (e) DTD denoising
    Measured spectral signal. (a) Initial signal; (b) corrected signal by WFPSI algorithm
    • Table 1. Attribute of spectral characteristic

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      Table 1. Attribute of spectral characteristic

      谱峰类型单峰多峰光谱值小波变换值一阶导数是否通过零点
      起始点A1A2最小值最小值×
      左拐点B1B2, F2上升沿最大值×
      谱峰C1C2, G2最大值最小值
      右拐点D1D2, H2下降沿最大值×
      波谷--E2最小值最小值
      结束点E1I2最小值最小值×
    • Table 2. Simulated spectral signal

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      Table 2. Simulated spectral signal

      谱峰位置(cm−1)64510901535213022002270
      谱峰高度369666
      半峰全宽253035404550
    • Table 3. SNR of signals after denoising using different methods

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      Table 3. SNR of signals after denoising using different methods

      原始信号硬阈值算法软阈值算法Savitzky-Golay滤波算法本文算法DTD
      SNR(dB)0.58.06218.32318.41068.9775
    • Table 4. Comparison of baseline removal performance among different algorithms

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      Table 4. Comparison of baseline removal performance among different algorithms

      基线类型PF算法AIRPLS算法SWMA算法本文算法WFPSI
      线型1.26290.31410.47510.3759
      高斯函数型1.07330.32900.48810.2883
      多项式型1.29860.65010.70760.6631
      e指数型1.27550.39530.47290.3489
      sigmoidal函数型1.25621.21781.23300.4520
    • Table 5. Percentage reduction in area of convex hull

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      Table 5. Percentage reduction in area of convex hull

      算法凸包面积减小百分比
      PF算法54.69%
      AIRPLS算法64.76%
      SWMA算法58.51%
      WFPSI算法66.21%
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    Fang QIAN, Yong-bo XU, Wei ZHAO. A spectrum signal pre-processing algorithm based on multi-scale wavelet transform[J]. Chinese Optics, 2025, 18(4): 794

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    Paper Information

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    Received: Dec. 26, 2024

    Accepted: Mar. 28, 2025

    Published Online: Aug. 13, 2025

    The Author Email:

    DOI:10.37188/CO.2024-0230

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