A Wavelet Denoising Method Based on Improved Threshold and Autocorrelation
【摘要】:In this paper, the noise of high frequency coefficients in wavelet decomposition is analyzed by autocorrelation function, which may determine which layer's wavelet high frequency coefficients to take part in wavelet reconstruction. The appropriate denoising threshold is selected according to noise in the signal. In considering of the advantages and disadvantages of hard threshold and soft threshold, an improved threshold function is designed. The wavelet high frequency coefficients of each layer are denoised By the improved threshold function and then reconstructed. Combining wavelet threshold denoising with wavelet decomposition and reconstruction, the denoising effectiveness is verified by MATLAB simulations, and the improved threshold function is proved to have better denoising effect than that of hard threshold and soft threshold.
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Firas Al Omari;[D];江苏大学;2014年 |
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Eidam Ahmed Hebiel Ahmed;[D];华中农业大学;2013年 |
3 |
Wu Zhiqiang;[D];中国海洋大学;2009年 |
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Isra'a Abdul-Ameer Abdul-Jabbar(伊娜);[D];合肥工业大学;2014年 |
5 |
Dileep Kumar;基于小波分析和神经网络的供水管网管内泄漏声检测方法研究[D];浙江大学;2018年 |
6 |
陆智萍;稳定与不稳定长记忆随机过程分析:估计、应用及预测[D];华东师范大学;2010年 |
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