小波域高斯混合模型方差估计近红外降噪方法

NIR Spectroscopy Noise Reduction Method Using Noise Variance Estimation by Gaussian Mixture Model in Wavelet Domain

针对抑制近红外光谱噪声与保留光谱信号细节的矛盾,提出一种基于噪声方差估计的小波域降噪方法。该法对光谱信号小波域高频系数建立了两状态高斯混合模型,用EM算法估计模型系数,推证模型对噪声方差准确估计特性,将估计得到的噪声方差建立了阈值降噪模型。实验建立黄酒近红外光谱快速预测酒精度偏最小二乘模型,对比分析Penalty阈值、Brige-Massart阈值和缺省阈值三种小波阈值降噪模型的降噪效果,验证了该法比上述三种常规阈值降噪模型具有更优的降噪效果,能有效应用于近红外光谱处理。

In order to solve the conflict between noise suppression and detail signal reservation of Near Infrared （NIR） spectroscopy,a wavelet domain noise reduction method is proposed based on the noise variance estimation.The method establishes two states Gaussian Mixture Models （GMM） of high-frequency coefficients in wavelet domain,uses Expectation Maximum （EM） algorithm to figure out the factor of model,proves that the model can accurately estimate noise variance,and puts the model to wavelet threshold noise suppression.By establishing the wine alcohol Partial Least Squares （PLS） prediction model of NIR spectroscopy,and comparing Penalty threshold,Brige-Massart threshold and the default threshold wavelet threshold denoising effect,the experiment validates that the method has better noise reduction effect than other normal method,which improves the stability of NIR model.