基于噪声方差估计的小波阈值降噪研究

信号中包含的噪声不仅降低了信号的质量,而且还严重影响着各种相关处理算法的有效性,因此,高效稳健的噪声方差估计对于各类信号处理非常重要。提出一种噪声方差估计的新方法,该方法首先应用两状态高斯混合模型对高频系数建模,混合模型的各项参数通过EM(Expectation-maximum)算法迭代估算得到。在建立的高斯混合模型中,当参数满足一定条件时,可以将高频系数分为噪声类和边缘类。基于高频子带内系数的相关性,对噪声类所包含的系数再次应用高斯混合模型的方法分类,并在每个类中分别进行噪声的估计,最后对所得噪声信号计算方差作为原始信号的噪声方差估计。基于这种估计方法,将小波阈值法应用到反求工程的降噪中,实际信号的降噪结果在光滑性和特征保持方面均有较好的效果。试验表明,该噪声方差估计方法对噪声大小具有一定适应性,且小波阈值降噪法简单易行,应用广泛。

Study on Wavelet Threshold Denoising Algorithm Based on Estimation of Noise Variance

Noise of signal not only reduces the quality of signal but also interferes the validity of correlative arithmetic seriously. Therefore,effective and robust estimation of noise variance is very important for various signal processing. A new method is proposed to estimate noise variance. A Gaussian mixture model (GMM) is used to model the high frequency wavelet coefficients (HFWC). The parameters of the mixture model are obtained with the EM iterative algorithm. The HFWC will be classified as noises class and...