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为探究小波分析对油菜籽红外光声光谱的去噪效果,利用db6小波4尺度分解对其进行去噪研究。对比分析低频系数重构、缺省阈值、Birge-Massart阈值和4种自适应阈值(Rigrsure、Minimaxi、Rigsure和Sqtwolog)模型等模型的去噪效果。同时与Savitzky-Golay卷积平滑和快速傅里叶变换的去噪效果进行比较。研究表明,Birg e-M assart阈值模型的综合小波去噪效果最好,同时小波去噪的方法较Savitzky-Golay卷积平滑和快速傅里叶变换去噪可以更好地捕获光谱的尖峰特征。
In order to explore the wavelet denoising effect on rapeseed infrared photoacoustic spectroscopy, this paper uses db6 wavelet 4-scale decomposition to denoise it. The effects of low frequency coefficient reconstruction, default thresholds, Birge-Massart thresholds and four adaptive thresholds (Rigrsure, Minimaxi, Rigsure and Sqtwolog) models were compared and analyzed. At the same time, it is compared with the denoising effect of Savitzky-Golay convolutional smoothing and fast Fourier transform. The results show that the integrated wavelet de-noising of the Birg e-M assart threshold model is the best, and the wavelet de-noising method can better capture the spectral peak features than the Savitzky-Golay convolution smoothing and fast Fourier transform denoising.