Method of weak signal recovery based on 3D-curvelet transform

Computer Engineering and Applications ›› 2016, Vol. 52 ›› Issue (18): 199-202.

Previous Articles Next Articles

Method of weak signal recovery based on 3D-curvelet transform

HU Yu1, XIE Kai1, RUAN Ningjun1, WU Peng2，3, HE Jianbiao4, LIU Conghao1

1.Institute for Oil & Gas Information Processing and Identification, Electronics & Information College, Yangtze University, Jingzhou, Hubei 434023, China
2.College of Information Engineering, China University of Geosciences, Wuhan 430074, China
3.National Engineering Research Center for Geographic Information System, Wuhan 430074, China
4.School of Information Science and Engineering, Central South University, Changsha 410083, China

Online:2016-09-15 Published:2016-09-14

基于三维曲波变换的弱信号恢复方法研究

胡雨1，谢凯1，阮宁君1，伍鹏2，3，贺建飚4，刘从浩1

1.长江大学电子信息学院油气信息处理与识别研究所，湖北荆州 434023
2.中国地质大学信息工程学院，武汉 430074
3.国家地理信息系统工程技术研究中心，武汉 430074
4.中南大学信息科学与工程学院，长沙 410083

Abstract

Abstract: Conventional methods of weak signal recovery are limited under strong noise condition. Useful information is often submerged in heavy noise, which is not easy to identify. As to this problem, a weak signal recovery method based on 3D-curvelet transform is proposed in this paper. Combining 3D-curvelet transform with the adaptive filter is to recover weak signal. Model of wedge and actual data are processed by this method. The experimental results show that the signal to noise ratio of data improves 2 dB to 3 dB and the frequency is increased 150 Hz. Obviously, the weak signal is recovered.

Key words: recovery of weak signal, 3D-curvelet transform, signal to noise ratio, adaptive optimal threshold, adaptive filter

摘要： 传统的弱信号恢复方法在强噪声背景下具有较大的局限性，有用的信息往往淹没在强噪声背景下不易被识别。针对这个难点，提出一种基于三维曲波变换的弱信号恢复的方法。该方法将三维曲波变换和自适应滤波器相融合，从而提高数据中弱信号的能量，使得弱信号更易于被恢复。为了验证该方法的有效性，对楔形模型与实际三维数据进行处理。实验结果表明，恢复后的数据信噪比提高了2 dB到3 dB，频带也被拓宽了150 Hz，弱信号得到较好的恢复。

关键词: 弱信号恢复, 三维曲波变换, 信噪比, 自适应最优阈值, 自适应滤波器

HU Yu1, XIE Kai1, RUAN Ningjun1, WU Peng2，3, HE Jianbiao4, LIU Conghao1. Method of weak signal recovery based on 3D-curvelet transform[J]. Computer Engineering and Applications, 2016, 52(18): 199-202.

胡雨1，谢凯1，阮宁君1，伍鹏2，3，贺建飚4，刘从浩1. 基于三维曲波变换的弱信号恢复方法研究[J]. 计算机工程与应用, 2016, 52(18): 199-202.

[1]	LIU Jianfeng, LI Ruihua, LIU Yaoqi, SU Yongtao, HU Jinlong. Research on Signal-to-Noise Ratio Estimation Algorithm in Wireless Communication [J]. Computer Engineering and Applications, 2020, 56(18): 82-89.
[2]	ZHANG Zhiyu1, LI Xiangyue1, LI Xiangyang2. Research on random noise suppression by synchrosqueezing wavelet transform [J]. Computer Engineering and Applications, 2018, 54(5): 57-60.
[3]	DING Wei1, LIU Heng2, LIAO Chengwang1. Design and implementation of timely monitoring system for microquake based on Android device [J]. Computer Engineering and Applications, 2018, 54(17): 266-270.
[4]	CHENG Zhao1，2, WANG Heming1, TANG Yongkang2, ZHANG Ying1. Detail enhancement analysis based hardware Trojan detection using thermal maps [J]. Computer Engineering and Applications, 2018, 54(16): 86-92.
[5]	PENG Zhiying, XIA Haibao, XU Yunshan. Adaptive generalized high-degree Cubature Kalman Filter based on target tracking [J]. Computer Engineering and Applications, 2018, 54(11): 46-52.
[6]	JIANG Julang1, ZHANG Han2, ZHU Zhu1, ZHAN Wenfa1. Polydirectional weighted mean filter for high-density salt & pepper noise [J]. Computer Engineering and Applications, 2016, 52(6): 204-208.
[7]	XUE Juntao, JIN Zhigang, YANG Zhengling, ZHANG Jun. Analysis of watermarking model based on similarity of vectors [J]. Computer Engineering and Applications, 2016, 52(24): 116-120.
[8]	CHEN Changlong, SUN Kehui. Fractional-order differential image enhancement algorithm based on image characteristics division [J]. Computer Engineering and Applications, 2016, 52(14): 186-191.
[9]	PAN Zhengrong, QIAO Zijian, ZHANG Ning, DAI Rui. Improved singular value de-noising method based on cross-correlation [J]. Computer Engineering and Applications, 2015, 51(15): 221-224.
[10]	XIAO Guiren, XIE Fangsen, HU Haijiang. Advanced Q-NLM algorithm application in medical image denoising [J]. Computer Engineering and Applications, 2015, 51(10): 174-176.
[11]	LIU Yufeng, YAO Jiafei. Acoustic feedback control using single channel blind source separation [J]. Computer Engineering and Applications, 2015, 51(10): 211-214.
[12]	WANG Yulin1, TIAN Xuelong1，2, GAO Xueli1. DSP realization of modified speech enhancement algorithm based on adaptive filters [J]. Computer Engineering and Applications, 2015, 51(1): 208-212.
[13]	XU Wenchao, WANG Guangyan, GENG Yanxiang, BAI Fang, FEI Teng. Speech enhancement algorithm based on spectral subtraction and variable-step LMS algorithm [J]. Computer Engineering and Applications, 2015, 51(1): 213-217.
[14]	ZHU Zhenjun1, LIN Ming1, SONG Yueli2. Research of noncoherent integration under fluctuating targets [J]. Computer Engineering and Applications, 2014, 50(2): 208-211.
[15]	QIAO Naosheng1，2, ZHANG Fen3. Image denoising method based on fusion of generalized total variation and Butterworth high pass filter [J]. Computer Engineering and Applications, 2014, 50(19): 20-22.

Method of weak signal recovery based on 3D-curvelet transform

基于三维曲波变换的弱信号恢复方法研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics