A Wavelet Threshold De-noising Algorithm Based on Empirical Mode Decomposition
A Wavelet Threshold De-noising Algorithm Based on Empirical Mode Decomposition
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发表时间：
2009
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通讯作者：
Hong-zhou Xu
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作者：
Hong-zhou Xu
小波基和阈值的选取对小波阈值去噪没有理论依据。提出了一种基于经验模式分解（EMD）的新方法.首先将含噪信号分解为多个本征模态函数（IMF）;其次，小波阈值去噪只对含有噪声的高频IMF信号进行去噪，将去噪结果与低频IMF信号重构得到去噪后的信号。该方法避免了去噪过程中高频信息的丢失，同时可以消除信号中可能存在的趋势部分。仿真结果表明，基于经验模态分解的小波阈值去噪方法优于传统的小波阈值去噪方法。
Wavelet base and threshold have no theoretical basis for choosing wavelet threshold de-noising. A new method based on empirical mode decomposition (EMD) is proposed. At first,noisy signal is decomposed to several intrinsic mode functions (IMF). Secondly,wavelet threshold de-noising only acts on the high frequency IMF which contain noise,the results and the low frequency IMF can reconstructed to obtain the denoised signal. This method avoids the high frequency information lost during the de-nosing process,meanwhile it can eliminate the tendency part that might exist in the signal. The simulation results show that wavelet threshold de-noising based on EMD has advantage over the traditional wavelet threshold de-noising methods.