Denoising algorithm of 0-OTDR signal based on curvelet transform with adaptive threshold

被引：5

作者：

Li, Desheng ^{[1
]}

Wang, Hao ^{[2
]}

Wang, Xuewei ^{[1
]}

Li, Xiang ^{[1
]}

Huang, Tianye ^{[1
]}

Ge, Mingfeng ^{[1
]}

Yin, Jie ^{[1
]}

Chen, Shaoxiang ^{[3
]}

Huang, Bao ^{[4
]}

Guan, Kai ^{[5
]}

He, Chongwen ^{[6
]}

Hu, Huixuan ^{[4
]}

Li, Kang ^{[3
]}

Lian, Zhenggang ^{[7
]}

机构：

[1] China Univ Geosci, Sch Mech Engn & Elect Informat, Wuhan 430074, Peoples R China

[2] Univ Cambridge, Dept Engn, Div Elect Engn, Cambridge, England

[3] Wuhan Huaray Precis Laser Co Ltd, Wuhan 430223, Peoples R China

[4] Raycus Fiber Laser Technol Co Ltd, Wuhan 430075, Peoples R China

[5] Tianjin LiM Laser Technol Co Ltd, Tianjin 300380, Peoples R China

[6] Harbin Welding Laser Intelligent Equipment Wuhan, Wuhan 430074, Peoples R China

[7] Yangtze Opt Elect Co Ltd, Wuhan 430074, Peoples R China

来源：

OPTICS COMMUNICATIONS | 2023年 / 545卷

关键词：

Phase-sensitive optical time-domain; reflectometry; Distributed fiber sensing; Curvelet transform; De-noising; ENHANCEMENT; OTDR;

D O I：

10.1016/j.optcom.2023.129708

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

In this paper, a denoising scheme based on curvelet transform is proposed to improve the signal-to-noise ratio (SNR) for vibration sensing in phase-sensitive optical time-domain reflectometry (0-OTDR) systems. The noise level can be estimated based on non-vibration images, which can be used to set the threshold for curvelet coefficients. We further optimize the threshold according to the amplitude distribution of the curvelet coefficients. In the experimental demonstration, when the optical pulse width is 100 ns, the SNR of location information of the 100 Hz vibration events can be increased by 6.93 dB, which proves the effectiveness of the proposed method.

引用

页数：6

共 50 条

[1] Adaptive Algorithm for Image Denoising Based on Curvelet Threshold
Youssif, Aliaa A. A.
Darwish, A. A.
Madbouly, A. M. M.
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2010, 10 (01): : 322 - 328
[2] A new adaptive algorithm for image denoising based on curvelet transform
Chen, Musheng
Cai, Zhishan
MIPPR 2013: REMOTE SENSING IMAGE PROCESSING, GEOGRAPHIC INFORMATION SYSTEMS, AND OTHER APPLICATIONS, 2013, 8921
[3] Denoising Algorithm for φ- OTDR System Based on Adaptive Wavelet Threshold
Wan, Donghui
Wan, Shengpeng
Yu, Junsong
LASER & OPTOELECTRONICS PROGRESS, 2024, 61 (21)
[4] SNR improvement for 0-OTDR with sparse representation denoising method
Li, Junchan
Wang, Yu
Liu, Xin
Bai, Qing
Jin, Baoquan
OPTICAL FIBER TECHNOLOGY, 2023, 76
[5] Seismic signal denoising method based on curvelet transform
Wu A.-D.
Zhao X.-L.
Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science), 2010, 34 (03): : 30 - 33
[6] Image denoising algorithm using adaptive shrinkage threshold based on shearlet transform
Chen, Xi
Sun, Hui
Deng, Chengzhi
FCST 2009: PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON FRONTIER OF COMPUTER SCIENCE AND TECHNOLOGY, 2009, : 254 - 257
[7] Image denoising based on adaptive fusion of curvelet transform and Total Variation
Bhadauria, H. S.
Dewal, M. L.
INTERNATIONAL JOURNAL OF SIGNAL AND IMAGING SYSTEMS ENGINEERING, 2011, 4 (04) : 220 - 227
[8] ADAPTIVE WATERMARK ALGORITHM BASED FAST CURVELET TRANSFORM
Shen, Zheng-Wei
Liao, Fu-Cheng
PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION, VOLS 1 AND 2, 2008, : 518 - 523
[9] Research on Applications of Curvelet Threshold in Seismic Signal Denoising
Yang, Hui
Zhang, Hua
PROCEEDINGS OF THE 2017 5TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND COMPUTING TECHNOLOGY (ICMMCT 2017), 2017, 126 : 1386 - 1391
[10] Pulmonary CT Image Denoising Algorithm Based on Curvelet Transform Criterion
Shi Zhen-gang
Li Qin-zi
PROCEEDINGS OF 2017 7TH IEEE INTERNATIONAL SYMPOSIUM ON MICROWAVE, ANTENNA, PROPAGATION, AND EMC TECHNOLOGIES (MAPE), 2017, : 520 - 524

← 1 2 3 4 5 →