Noise reduction method research based on discrete wavelet transform

Yong Hong Wu; Quan Pan; Hong Cai Zhang; Jin Li Meng

Noise reduction method research based on discrete wavelet transform

Yong Hong Wu, Quan Pan, Hong Cai Zhang, Jin Li Meng

自动化学院

Northwestern Polytechnical University Xian

科研成果: 期刊稿件 › 文章 › 同行评审

1 引用（Scopus）

摘要

After several discrete wavelet transforms, another expression of the deviation of white noise on every scale is given by us. Then it is applied to wavelet denoising by the hard threshold. The simulation shows that the cost of dyadic wavelet transform is greater than the one of discrete wavelet transform, and the denoising quality of the former is better than that of the latter. At last the reason is given.

源语言	英语
页（从-至）	2706-2708+2712
期刊	Xitong Fangzhen Xuebao / Journal of System Simulation
卷	16
期	12
出版状态	已出版 - 12月 2004

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引用此

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abstract = "After several discrete wavelet transforms, another expression of the deviation of white noise on every scale is given by us. Then it is applied to wavelet denoising by the hard threshold. The simulation shows that the cost of dyadic wavelet transform is greater than the one of discrete wavelet transform, and the denoising quality of the former is better than that of the latter. At last the reason is given.",

keywords = "Deviation, Discrete wavelet transform, Dyadic wavelet transform, Hard threshold, Noise reduction",

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AU - Wu, Yong Hong

AU - Pan, Quan

AU - Zhang, Hong Cai

AU - Meng, Jin Li

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N2 - After several discrete wavelet transforms, another expression of the deviation of white noise on every scale is given by us. Then it is applied to wavelet denoising by the hard threshold. The simulation shows that the cost of dyadic wavelet transform is greater than the one of discrete wavelet transform, and the denoising quality of the former is better than that of the latter. At last the reason is given.

AB - After several discrete wavelet transforms, another expression of the deviation of white noise on every scale is given by us. Then it is applied to wavelet denoising by the hard threshold. The simulation shows that the cost of dyadic wavelet transform is greater than the one of discrete wavelet transform, and the denoising quality of the former is better than that of the latter. At last the reason is given.

KW - Deviation

KW - Discrete wavelet transform

KW - Dyadic wavelet transform

KW - Hard threshold

KW - Noise reduction

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SN - 1004-731X

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ER -

Noise reduction method research based on discrete wavelet transform

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