Terahertz time-domain spectral signal noise reduction analysis based on DG-SSA-VMD algorithm

Hao Guan; Zhenwei Zhang; Cunlin Zhang; Yuejin Zhao; Jing Xu

doi:10.1117/12.2688912

26 November 2023 Terahertz time-domain spectral signal noise reduction analysis based on DG-SSA-VMD algorithm

Hao Guan, Zhenwei Zhang, Cunlin Zhang, Yuejin Zhao, Jing Xu

Proceedings Volume 12776, Infrared, Millimeter-Wave, and Terahertz Technologies X; 1277619 (2023) https://doi.org/10.1117/12.2688912
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

When using the terahertz pulse signal for thickness detection, the system echo response directly affects the extraction of effective information in the signal. The double Gaussian inverse filtering (DGIF) method can effectively separate the sample reflection signal and the system echos. However, DGIF method will introduce high-frequency noise, seriously degrade the signal quality, and affect the detection result. In this paper, variational mode decomposition (VMD) combining DGIF algorithm and sparrow search algorithm (SSA) is used to denoise the signals of terahertz time-domain spectroscopy system. Firstly, the DGIF method is used to reconstruct the sample signal. Secondly, using the minimum envelope entropy as fitness value, the SSA was used to optimize the key parameters of VMD. Thirdly, the reconstructed terahertz time-domain spectral signal is decomposed by the optimization parameters to obtain intrinsic mode function. The kurtosis value and energy entropy of intrinsic mode function (IMF) is further extracted as the comprehensive evaluation index. The signal synthesized by the main mode functions is the reconstructed time domain signal after noise reduction. A quartz standard specimen was measured and compared with the original signal, DGIF signal, VMD signal and DG-SSA-VMD signal. Combined with the SNR and RMSE evaluation indexes, it is verified that the DG-SSA-VMD method can avoid the inconvenience of manually selecting key parameters, reduce the thickness extraction error, and has the best comprehensive denoising effect, which is conducive to further detection and imaging applications.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hao Guan, Zhenwei Zhang, Cunlin Zhang, Yuejin Zhao, and Jing Xu "Terahertz time-domain spectral signal noise reduction analysis based on DG-SSA-VMD algorithm", Proc. SPIE 12776, Infrared, Millimeter-Wave, and Terahertz Technologies X, 1277619 (26 November 2023); https://doi.org/10.1117/12.2688912

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