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<p>In order to avoid that the spectrometer is interfered by matrix elements, stray light and dark current in the spectral signal acquisition, which generates noise and affects the signal accuracy, a spectral denoising method based on the improved crested porcupine optimizer (ICPO) optimized variational mode decomposition (VMD) is proposed in this study. The method first uses ICPO to determine the optimal decomposition parameters of the VMD, then brings the optimal parameters into the VMD decomposition and extracts high-quality modal components for reconstruction through sample entropy to obtain the denoised spectral signal. The experimental results show that the method can effectively remove the noise in the spectral data, and the noise reduction performance is better than the other methods, providing a reliable basis for analyzing spectral signals. Meanwhile, to explore the good effect of improving the ICPO algorithm, other optimization algorithms are introduced for comparative analysis, and the results show that the optimization effect of the ICPO algorithm in the CEC2022 test set is better than that of other comparison algorithms.</p>

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Study on VMD decomposition and denoising of spectral signals based on improved CPO algorithm

  • 期刊论文

  • 发表日期：

    2025年2月25日

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  • Qinggao Zheng,
  • Da Qiu,
  • Junyi Chen,
  • Jianqiang Zhang,
  • Hang Gao,
  • Yang Long,
  • Song Liu,
  • Tingting Zhang,
  • Shiqiang Chen

摘要

In order to avoid that the spectrometer is interfered by matrix elements, stray light and dark current in the spectral signal acquisition, which generates noise and affects the signal accuracy, a spectral denoising method based on the improved crested porcupine optimizer (ICPO) optimized variational mode decomposition (VMD) is proposed in this study. The method first uses ICPO to determine the optimal decomposition parameters of the VMD, then brings the optimal parameters into the VMD decomposition and extracts high-quality modal components for reconstruction through sample entropy to obtain the denoised spectral signal. The experimental results show that the method can effectively remove the noise in the spectral data, and the noise reduction performance is better than the other methods, providing a reliable basis for analyzing spectral signals. Meanwhile, to explore the good effect of improving the ICPO algorithm, other optimization algorithms are introduced for comparative analysis, and the results show that the optimization effect of the ICPO algorithm in the CEC2022 test set is better than that of other comparison algorithms.