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天体光谱信号去噪的小波域复合阈值新算法
引用本文:赵瑞珍,胡占义,胡绍海.天体光谱信号去噪的小波域复合阈值新算法[J].光谱学与光谱分析,2007,27(8):1644-1647.
作者姓名:赵瑞珍  胡占义  胡绍海
作者单位:1. 北京交通大学计算机与信息技术学院,北京 100044
2. 中国科学院自动化研究所模式识别国家重点实验室,北京100080
基金项目:国家自然科学基金 , 北京交通大学校科研和教改项目
摘    要:利用谱线和噪声在小波域内的不同相关特性,提出了一种小波域复合阈值去噪算法。首先将小波系数作NeighShrink阈值处理,然后对得到的小波系数进行二值化,在此基础上定义了每一小波系数所对应的横向相关性指数和纵向相关性指数,最后确定出决定小波系数取舍的决策系数。由于该决策系数是通过多重判据得到的,因此该方法克服了简单阈值方法过保留或过扼杀的缺点,同时可以去除大脉冲噪声,实验结果表明了该方法的有效性。

关 键 词:天体光谱  小波变换  复合阈值  去噪  
文章编号:1000-0593(2007)08-1644-04
收稿时间:2006-04-20
修稿时间:2006-04-20

A Novel Wavelet Multiple Thresholding Algorithm for Astronomical Spectral Signal Denoising
ZHAO Rui-zhen,HU Zhan-yi,HU Shao-hai.A Novel Wavelet Multiple Thresholding Algorithm for Astronomical Spectral Signal Denoising[J].Spectroscopy and Spectral Analysis,2007,27(8):1644-1647.
Authors:ZHAO Rui-zhen  HU Zhan-yi  HU Shao-hai
Institution:1. School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China2. National Key Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100080,China
Abstract:By using the different relativities of spectral lines and noises in the wavelet domain,a novel wavelet multiple thresholding algorithm is presented in the present paper for astronomical spectra with low signal-noise-ratio(SNR).Firstly the wavelet coefficients are estimated by NeighShrink approach and then 0-1 coefficients are obtained.Based on the above binary coefficients,two kinds of relativity exponents at each scale and across scales respectively are defined for each wavelet coefficients.Finally decision coefficients are determined according to the magnitudes of the relativity exponents.This algorithm overcomes the over-reserving or over-shrinking disadvantages of the simple threshold method in that the decision coefficients are obtained by multiple criteria.Besides,large pulse noises can be removed by the presented algorithm because it takes spectral line features into consideration.The experimental results show that the proposed algorithm is computationally efficient and practical.
Keywords:Astronomical spectra  Wavelet transform  Multiple threshold  Denoising
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