EEMD自适应去噪在拉曼光谱中的应用

二代小波是公认较好的降噪手段，但是降噪效果依赖于基函数、分解层数和阈值等参数设置。经验模态分解(empirical mode decomposition, EMD)无需参数设定，按照频率特性将信号分解成本征模函数(intrinsic mode function, IMF)，对IMF滤波，实现了信号自适应去噪。拉曼光谱中信号和噪声交叠集中在极高频段，EMD产生模态混叠问题，影响去噪效果。应用总体平均经验模态分解(ensemble empirical mode decomposition，EEMD)拉曼光谱克服了模态混叠，有效区分出高频信号和噪声，获得了与小波函数相似去噪效果。文中首先对一段非线性非平稳豆油脂拉曼光谱EMD分解，可见模态混叠，EEMD分解出清晰模态的特征分量。然后分别用快速傅里叶变换(fast Fourier transform，FFT)、小波变换(Wavelet)、EMD和EEMD处理含噪光谱，信噪比、均方根误差、相关系数三个方面指标表明FFT高频去噪效果最差，其次是EMD，恰当的Wavelet同EEMD效果相当，EEMD的优势是降噪过程的自适应。最后提出光谱时频分析方法和IMF噪声属性判别准则研究趋势。

EEMD De-Noising Adaptively in Raman Spectroscopy

It is well known that the second generation wavelet is the best de-noising means, but the result of de-noising depends on how to set up the basis function, decomposition layers and threshold parameters. Without parameter setting empirical mode decomposition (EMD) decomposes the signal into intrinsic mode functions (IMF)，then structuring IMF filter and the de-noising process is adaptive. It is worth noting that the signal and the noise are mixed together in very high frequency, that is to say that there has been mode overlap, and what happened will affect the de-noising effect. It was found that ensemble empirical mode decomposition (EEMD) decomposes Raman spectrum into the signal and the noise effectively avoiding from mode overlap in high frequency in the experiments, and it is similar with wavelet in de-nosing effect fortunately. At first, a period of non-linear and non-smooth bean greases Raman spectrum was decomposed by EMD in the paper, there was mode overlap, but the authors have got clear characteristic components by EEMD. Secondly noisy spectrum was processed by fast Fourier transform (FFT), wavelet, EMD and EEMD independently, and signal to noise ratio，root mean square error and correlation coefficient indicate that FFT is the worse means in high frequency de-noising than EMD, and the appropriate wavelet is similar with EEMD in de-noising result, but the de-noising process of EEMD is adaptive. In the last section, a brief research direction of the spectrum study method in time frequency field and noise properties criterion on IMF are given for the future.