摘要
提出了一种基于卷积型小波包变换的多尺度降噪方法。采用卷积型小波包变换,克服了传统小波包变换数据点数随分解尺度的增加而呈指数减小的问题;改进了噪声方差估计方法,较好地保留了信号的主要细节;采用了新的阈值函数,其表达式简单易于计算,同Donoho软阈值函数具有相同的连续性,且克服了软阈值函数中估计小波系数与分解小波系数之间存在着恒定偏差的问题。仿真结果表明,新的降噪方法有效抑制了在信号奇异点附近产生的Pseudo-Gibbs现象,在降噪精度上优于传统的小波包降噪方法。
A multi-scale de-noising algorithm based on the convolution type of wavelet packet transformation was presented. This algorithm overcame shortcomings of the classical wavelet packet transformation, in which the length of sequences obtained always decreased by decomposition scales. The new algorithm improved estimated method of white noise standard deviation at each scale and thus kept the main edges of signal well. A new threshold function has been employed in this algorithm, which was simple in expression and as continuous as the Donoho＇s soft threshold function, and overcame the shortcoming of an invariable dispersion between the estimated wavelet coefficients and the decomposed wavelet coefficients of the softthreshold method. Simulation results indicated that the new de-noising method suppressed the Pseudo-Gibbs phenomena near the singularities of the signal effectively and achieved better SNR gains than de-noising method based on classical wavelet packet transformation.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2007年第12期160-164,共5页
Transactions of the Chinese Society for Agricultural Machinery
基金
浙江省科技计划重点项目(项目编号:2006C21063)
关键词
信号降噪
卷积型小波包变换
阈值函数
噪声估计
Signal de-nosing, Convolution type of wavelet packet transformation, Threshold function, Noise estimation
