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阈值和阈值函数的选取是小波去噪的关键。经小波分解变换后,低频部分包含大量的有用信号,而噪声分布于整个小波域内的高频部分,其中包含少数有用信号的细节信息。采用固定阈值和阈值函数处理时会造成高频部分的有用信号细节信息丢失。基于邻域小波变换将分解层数引入阈值和阈值函数中,使用动态阈值和阈值函数处理不同分解层数下的小波系数,来保留不同层数下的细节信息。仿真结果表明所提方法的信噪比和信噪比增益最大,且均方差最小,相关系数逼近于1,原始信号与重构信号相似度得到提高,改善了去噪效果。
The selection of threshold and threshold function is the key of wavelet denoising. After wavelet transform transform, the low frequency part contains a large number of useful signals, while the noise is distributed in the high frequency part of the entire wavelet domain, which contains the details of a few useful signals. Use of fixed thresholds and threshold functions can result in the loss of useful signal detail information for the high frequency components. Based on neighborhood wavelet transform, the decomposition level is introduced into the threshold and threshold functions, and the dynamic threshold and threshold functions are used to deal with the wavelet coefficients at different decomposition levels to preserve the detail information under different layers. The simulation results show that the proposed method has the highest signal-to-noise ratio and signal-to-noise ratio gain with the smallest mean square error, the correlation coefficient approaches 1, the similarity between the original signal and the reconstructed signal is improved, and the denoising effect is improved.