Noise level estimation of BOTDA for optimal non-local means denoising

被引:45
作者
Qian, Xianyang [1 ,2 ]
Jia, Xinhong [3 ]
Wang, Zinan [1 ,2 ]
Zhang, Bin [1 ]
Xue, Naitian [1 ]
Sun, Wei [1 ]
He, Qiheng [1 ]
Wu, Han [1 ]
机构
[1] Univ Elect Sci & Technol China, Educ Minist China, Key Lab Opt Fiber Sensing & Commun, Chengdu 611731, Peoples R China
[2] Univ Elect Sci & Technol China, Ctr Informat Geosci, Chengdu 611731, Peoples R China
[3] Sichuan Normal Univ, Sch Phys & Elect Engn, Chengdu 610068, Peoples R China
基金
中国国家自然科学基金;
关键词
FIBER SENSOR; IMAGE; AMPLIFICATION; OTDR;
D O I
10.1364/AO.56.004727
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Due to the similarity of Brillouin optical time domain analyzer (BOTDA) signals, image denoising could be utilized to remove the noise. However, the performance can be much degraded due to inaccurate noise level estimation. By numerical and experimental study, we compare the noise level estimation of three different methods for BOTDA: calculating the standard deviation (STD) of the measurements, a filter-based estimation algorithm, and a patch-based estimation algorithm proposed in this paper, which selects weak textured patches of BOTDA signal and then estimates noise level using principal component analysis (W-PCA). The results show that W-PCA and the mean of STD can accurately estimate the noise level, while the filter-based method overestimates the noise level. Nevertheless, for BOTDA with distributed amplification, the STD has huge fluctuation along the length, while the W-PCA is relatively robust for its global consideration. Experimental results of an ultralong-distance BOTDA prove that the non-local means denoising processing based on W-PCA effectively removes the noise of a sensing system without signal distortion. (C) 2017 Optical Society of America
引用
收藏
页码:4727 / 4734
页数:8
相关论文
共 33 条
[1]   Signal processing using artificial neural network for BOTDA sensor system [J].
Azad, Abul Kalam ;
Wang, Liang ;
Guo, Nan ;
Tam, Hwa-Yaw ;
Lu, Chao .
OPTICS EXPRESS, 2016, 24 (06) :6769-6782
[2]  
Bowman A.W., 1997, Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations, V18
[3]   A review of image denoising algorithms, with a new one [J].
Buades, A ;
Coll, B ;
Morel, JM .
MULTISCALE MODELING & SIMULATION, 2005, 4 (02) :490-530
[4]   Nonlocal image and movie denoising [J].
Buades, Antoni ;
Coll, Bartomeu ;
Morel, Jean-Michel .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2008, 76 (02) :123-139
[5]   Adaptive wavelet thresholding for image denoising and compression [J].
Chang, SG ;
Yu, B ;
Vetterli, M .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2000, 9 (09) :1532-1546
[6]   Signal-to-Noise Ratio Improvement in BOTDA Using Balanced Detection [J].
Dominguez-Lopez, Alejandro ;
Lopez-Gil, Alexia ;
Martin-Lopez, Sonia ;
Gonzalez-Herraez, Miguel .
IEEE PHOTONICS TECHNOLOGY LETTERS, 2014, 26 (04) :338-341
[7]   Extending the Sensing Range of Brillouin Optical Time-Domain Analysis Combining Frequency-Division Multiplexing and In-Line EDFAs [J].
Dong, Yongkang ;
Chen, Liang ;
Bao, Xiaoyi .
JOURNAL OF LIGHTWAVE TECHNOLOGY, 2012, 30 (08) :1161-1167
[8]   DE-NOISING BY SOFT-THRESHOLDING [J].
DONOHO, DL .
IEEE TRANSACTIONS ON INFORMATION THEORY, 1995, 41 (03) :613-627
[9]   DEVELOPMENT OF A DISTRIBUTED SENSING TECHNIQUE USING BRILLOUIN-SCATTERING [J].
HORIGUCHI, T ;
SHIMIZU, K ;
KURASHIMA, T ;
TATEDA, M ;
KOYAMADA, Y .
JOURNAL OF LIGHTWAVE TECHNOLOGY, 1995, 13 (07) :1296-1302
[10]   Hybrid distributed Raman amplification combining random fiber laser based 2nd-order and low-noise LD based 1st-order pumping [J].
Jia, Xin-Hong ;
Rao, Yun-Jiang ;
Yuan, Cheng-Xu ;
Li, Jin ;
Yan, Xiao-Dong ;
Wang, Zi-Nan ;
Zhang, Wei-Li ;
Wu, Han ;
Zhu, Ye-Yu ;
Peng, Fei .
OPTICS EXPRESS, 2013, 21 (21) :24611-24619