Denoising reconstruction of Raman spectra based on wavelet transform and weighted Wiener estimation

Raman spectra are widely used in various fields, so it is crucial to realize the fast detection of Raman spectra. The removal of noise in Raman spectra is one of the difficulties in spectroscopic research, and this paper proposes a denoising and reconstruction method for spectral signals based on wavelet transform and weighted Wiener estimation. The wavelet transform is used to remove high-frequency noise, and then the Raman spectrum is reconstructed with the help of weighted Wiener estimation. The spectra in the range of 600-1800 are denoised and reconstructed using samples of body membranes and cells as research objects. The results show that the wavelet transform can effectively remove the noise from the spectra, while the weighted Wiener estimation can reconstruct the spectra accurately. Therefore, the spectral denoising reconstruction method based on wavelet transform and weighted Wiener estimation can improve the resolution of spectral reconstruction and show significant advantages in spectral reconstruction.