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Laser & Optoelectronics Progress, Volume. 62, Issue 1, 0100009(2025)

Research Progress on Signal Noise Reduction Methods in Distributed Fiber Optic Sensing Technology Based on Brillouin Scattering

Haoran Gao1,2、*, Yongzheng Li2,3, Linfeng Guo1,2,4, and Xiaomin Xu4,5
Author Affiliations
  • 1School of Physics and Optoelectronic Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu , China
  • 2Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean, Nanjing 210044, Jiangsu , China
  • 3China Railway No.3 Group East China Construction Co., Ltd, Nanjing 211153, Jiangsu , China
  • 4Jiangsu International Joint Laboratory on Meterological Photonics and Optoelectronic Detection, Nanjing 210044, Jiangsu , China
  • 5Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
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    AbstractGet PDF(in Chinese)
    Figures&Tables (15)
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    References (62)
    Cited By (2)
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    Figures & Tables(15)
    Principle diagrams of BOTDR and BOTDA[1]. (a) BOTDR; (b) BOTDA

    Fig. 1. Principle diagrams of BOTDR and BOTDA[1]. (a) BOTDR; (b) BOTDA

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    Schematic of three-level wavelet decomposition

    Fig. 2. Schematic of three-level wavelet decomposition

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    Flow chart of EMD-FIR algorithm[28]

    Fig. 3. Flow chart of EMD-FIR algorithm[28]

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    Flow chart of PSO finding the optimal threshold[31]

    Fig. 4. Flow chart of PSO finding the optimal threshold[31]

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    Improvement of SNR by four image processing techniques[32]. (a) GS; (b) NLM; (c) DCT; (d) DWT

    Fig. 5. Improvement of SNR by four image processing techniques[32]. (a) GS; (b) NLM; (c) DCT; (d) DWT

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    Improvement of frequency uncertainty by four image processing techniques[32]. (a) GS; (b) NLM; (c) DCT; (d) DWT

    Fig. 6. Improvement of frequency uncertainty by four image processing techniques[32]. (a) GS; (b) NLM; (c) DCT; (d) DWT

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    Flow chart of BM3D noise reduction in BOTDA[41]

    Fig. 7. Flow chart of BM3D noise reduction in BOTDA[41]

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    Flow chart of AAD algorithm in BOTDA[46]

    Fig. 8. Flow chart of AAD algorithm in BOTDA[46]

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    BSP noise reduction algorithm flow chart in BOTDA[49]

    Fig. 9. BSP noise reduction algorithm flow chart in BOTDA[49]

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    Structure of DCAE[53]

    Fig. 10. Structure of DCAE[53]

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    Training process of DnCNN[54]

    Fig. 11. Training process of DnCNN[54]

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    Basic architecture diagram of FastDVDnet[58]

    Fig. 12. Basic architecture diagram of FastDVDnet[58]

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    Network architecture of ADNet[61]

    Fig. 13. Network architecture of ADNet[61]

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    Brillouin frequency shift distribution curves of SAID and different noise reduction algorithms[62]. (a) Overall comparison; (b) detailed comparison of frequency offset areas

    Fig. 14. Brillouin frequency shift distribution curves of SAID and different noise reduction algorithms[62]. (a) Overall comparison; (b) detailed comparison of frequency offset areas

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    • Table 1. Characteristics and system technical indicators of various noise reduction algorithms

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      Table 1. Characteristics and system technical indicators of various noise reduction algorithms

      Method classificationMethodDistance /kmSNR improvement /dBCharacteristic
      1D signal denoising algorithmWD115.5Fast processing speed， requires estimation of noise characteristics
      ALP-FIR115Enhance noise reduction robustness， but the design is complex
      EMD-FIR611.69Strong adaptability to signals and ability to handle non-stationary signals
      LMD2313Shortened time compared to EMD
      CEEMD-PSO22.519.7Signal decomposition and threshold selection are both adaptive
      Image and video denoising technologyGS507.92Causing missing details
      NLM5012.16The measurement accuracy of the deteriorated temperature rise part
      DCT5012.86‒
      DWT5013.93Fast processing speed
      3D-NLM5020.7‒
      BM3D62.313The spatial resolution and measurement accuracy have almost no impact， but the calculation is complex
      VBM3D10013.2‒
      AD9916Deteriorating measurement accuracy
      AAD9915
      QWT4014.72Fast processing speed
      BSR5.2‒Using different algorithms for processing based on the division of Brillouin spectra
      BSP0.8‒
      FDF5.2‒
      Deep learning techniquesANN116‒22Suitable for embedding FPGA
      DAE40.179.96Has good robustness to noise
      DACE19.3811.2Introduced attention mechanism to extract more robust noise information
      DnCNN1013.57Preserves the characteristics of the signal and eliminates the need for high sampling speed devices
      FastDVDnet1010.85More effectively utilizing the time redundancy of adjacent data
      DANet11.519.08No need to design image priors and noise distribution assumptions
      ADNet‒30Slight improvement in standard deviation and root mean square error
      SAID25.121.92Improved adaptability to images and avoided excessive smoothing
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    Haoran Gao, Yongzheng Li, Linfeng Guo, Xiaomin Xu. Research Progress on Signal Noise Reduction Methods in Distributed Fiber Optic Sensing Technology Based on Brillouin Scattering[J]. Laser & Optoelectronics Progress, 2025, 62(1): 0100009

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    Paper Information

    Category: Reviews

    Received: Apr. 22, 2024

    Accepted: May. 22, 2024

    Published Online: Jan. 6, 2025

    The Author Email:

    DOI:10.3788/LOP241115

    CSTR:32186.14.LOP241115

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology