THz spectrum processing method based on optimal wavelet selection

Abstract

Terahertz spectrum is easily interfered by system noise and water-vapor absorption. In order to obtain high quality spectrum and better prediction accuracy in qualitative and quantitative analysis model, different wavelet basis functions and levels of decompositions are employed to perform denoising processing. In this study, the terahertz spectra of wheat samples are denoised using wavelet transform. The compound evaluation indicators (T) are used for systematically analyzing the quality effect of wavelet transform in terahertz spectrum preprocessing. By comparing the optimal denoising effects of different wavelet families, the wavelets of coiflets and symlets are more suitable for terahertz spectrum denoising processing than the wavelets of fejer-korovkin and daubechies, and the performance of symlets 8 wavelet basis function with 4-level decomposition is the optimum. The results show that the proposed method can select the optimal wavelet basis function and decomposition level of wavelet denoising processing in the field of terahertz spectrum analysis.