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Lifting wavelet de-noising method with dual-threshold based on PSO algorithm

机译:基于PSO算法的双阈值提升小波降噪方法

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To remove the noise of signal and improve the signal-to-noise ratio, we present a lifting wavelet de-noising method with flexible dual-threshold based on PSO algorithm. We use the lifting wavelet instead of traditional wavelet to decompose the signal, in order to improve the operation speed. we use the quantization function by flexible dual-threshold to quantify the detail coefficients. By doing this, we preferably retained the fine features of the signal, while preventing the signal oscillation. PSO algorithm is used to optimize the dual-threshold, in order to get the optimal threshold value, to improve the signal-to-noise ratio. The simulation and experimental results show that this new de-noising method can effectively suppress the noise, and get a higher signal-to-noise ratio and faster processing speed compared to the traditional denoising method.
机译:为了消除信号噪声并提高信噪比,提出了一种基于PSO算法的具有灵活双阈值的提升小波去噪方法。为了提高运算速度,我们使用提升小波代替传统的小波对信号进行分解。我们通过灵活的双阈值使用量化函数对细节系数进行量化。通过这样做,我们最好保留信号的精细特征,同时防止信号振荡。 PSO算法用于优化双阈值,以获得最佳阈值,以提高信噪比。仿真和实验结果表明,与传统的降噪方法相比,这种新的降噪方法可以有效地抑制噪声,并具有更高的信噪比和更快的处理速度。

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