Genetic Optimized Algorithms in Wavelet Thresholding De-noising

被引：0
|
作者：
Zhao, Qi [1 ]
Liu, Yi [1 ]
机构：
[1] Hebei Univ Engn, Inst Informat & Elect Engn, Handan, Peoples R China
来源：
2009 ISECS INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT, VOL III | 2009年
关键词：
wavelet transform; thresholding de-noising; genetic optimization;
D O I：
10.1109/CCCM.2009.5267873
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Combined with the characteristics of soft and hard thresholding de-noising methods, this paper posed an improved threshold quantifying project, added the estimated factor, used genetic algorithms to optimize the estimated factor, the fitness function is the signal to noise ratio. The improved project applied to the test signal added noise, the result show that this project compares with soft and hard thresholding de-noising methods makes the de-noising better in a certain extent, and it enhances the signal to noise ratio of de-noising signal.
引用
收藏
页码：173 / 176
页数：4
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