Denoising of X-ray pulsar observed profile in the undecimated wavelet domain
Denoising of X-ray pulsar observed profile in the undecimated wavelet domain
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未抽取小波域中 X 射线脉冲星观测轮廓的去噪
DOI：
10.1016/j.actaastro.2015.09.018
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发表时间：
2016
影响因子：
3.5
通讯作者：
Shen, Li-rong
中科院分区：
文献类型：
--
作者：
Fu, Ling-zhong;Liu, Xiu-ping;Sun, Hai-feng;Shen, Li-rong
由于X射线脉冲星信号强度低，背景辐射强，导致通过历元折叠得到的X射线脉冲星观测剖面信噪比低，特别是在观测时间不够长的情况下。这表明有必要对观察到的轮廓进行去噪。本文研究了X射线脉冲星信号的统计特性，建立了观测剖面的信号相关噪声模型。在此基础上，提出了一种在非抽取小波域进行局部线性最小均方误差滤波的轮廓降噪方法。通过将细节小波系数的幅度乘以局部自适应因子来重新缩放细节小波系数，该局部自适应因子是无噪声系数与噪声系数的局部方差比。该算法所需的所有非平稳统计量都是从观测到的轮廓中计算出来的，而不需要先验信息。对地面模拟系统获得的模拟数据和Rossi X射线定时探测器卫星获得的真实的数据进行的实验结果表明，该方法在抑制噪声和保持峰值锐度方面都有很好的效果，并且在信噪比、皮尔逊相关系数和均方根误差方面明显优于四种广泛接受和使用的小波去噪方法。
The low intensity of the X-ray pulsar signal and the strong X-ray background radiation lead to low signal-to-noise ratio (SNR) of the X-ray pulsar observed profile obtained through epoch folding, especially when the observation time is not long enough. This signifies the necessity of denoising of the observed profile. In this paper, the statistical characteristics of the X-ray pulsar signal are studied, and a signal-dependent noise model is established for the observed profile. Based on this, a profile noise reduction method by performing a local linear minimum mean square error filtering in the un-decimated wavelet domain is developed. The detail wavelet coefficients are rescaled by multiplying their amplitudes by a locally adaptive factor, which is the local variance ratio of the noiseless coefficients to the noisy ones. All the nonstationary statistics needed in the algorithm are calculated from the observed profile, without a priori information. The results of experiments, carried out on simulated data obtained by the ground-based simulation system and real data obtained by Rossi X-Ray Timing Explorer satellite, indicate that the proposed method is excellent in both noise suppression and preservation of peak sharpness, and it also clearly outperforms four widely accepted and used wavelet denoising methods, in terms of SNR, Pearson correlation coefficient and root mean square error.
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影响因子：
--
作者：
K. Daqrouq;I. Abu-Isbeih;O. Daoud;Emad Khalaf
通讯作者：
K. Daqrouq;I. Abu-Isbeih;O. Daoud;Emad Khalaf
影响因子：
6.5
作者：
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通讯作者：
P. Caraveo;R. Mignani
DOI：
10.7498/aps.61.019701
发表时间：
2012-01
期刊：
--
影响因子：
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作者：
Hu Huijun;Z. Baosheng;Sheng Lizhi;Yan Qiurong;Chen Baomei;Wang Peng
通讯作者：
Hu Huijun;Z. Baosheng;Sheng Lizhi;Yan Qiurong;Chen Baomei;Wang Peng
影响因子：
6.5
作者：
D. Matsakis;J. Taylor;T. Eubanks
通讯作者：
D. Matsakis;J. Taylor;T. Eubanks
DOI：
--
发表时间：
2009-01
期刊：
--
影响因子：
--
作者：
S. Sheikh;J. Hanson;J. Collins;P. Graven
通讯作者：
S. Sheikh;J. Hanson;J. Collins;P. Graven