A Study of Raman Spectra Denoising Based on Empirical Mode Decomposition

The multiresolution empirical mode decomposition (EMD) is a new method of analyzing signal. It can be interpreted as a temporal and spatial filtering based on the extremum characteristic scale of the signal. This method can preserve the nonlinearity and non-stability of signal, and has potential superiority in filtering and denoising. In the present paper, a new denoising method of Raman spectra on the basis of empirical mode decomposition with multiresolution filtering is presented. First, Raman spectra polluted by the white. noise is decomposed into several intrinsic mode function (IMF) components of different time scale based on empirical mode decomposition ( EMD). Then. the IMF components of high frequencies Fire preprocessed using the threshold method, and we add these IMF components to the IMF components of low frequencies to achieve denoising signal. For various noise levels, the effects of three methods (the EMD threshold denoising, the wavelet threshold denoising and the EMD low-pass filtering) are analyzed by processing the noisy p-xylene spectra. The results show that the EMD threshold denosing method eliminates the noise effectively. In addition, this method also preserves the detailed information of the original spectra well. By contrast with the wavelet threshold denosing method, the EMD threshold denoising requires no prior knowledge about the sample composition and no selection of the suitable decomposition level, and the adaptation is in evidence. The new method will have a good application prospect in Raman spectra denoising.