Abstract

We have experimentally analyzed and compared the performance of Brillouin optical time-domain analyzer (BOTDA) sensors assisted by non-local means (NLM) and wavelet denoising (WD) techniques in terms of measurement accuracy and experimental spatial resolution, respectively. Degradation of the measurement accuracy and experimental spatial resolution after denoising by NLM and WD are observed, which originate from the fact that higher signal-to-noise ratio (SNR) improvement is achieved at the expense of sacrificing the details of BOTDA data, and smaller data sampling point number (SPN) gives rise to insufficient redundant information for denoising. The two parameters degrade to different extents depending on the amount of SNR improvement and SPN adopted in data acquisition. Compared with WD, NLM relies more on the features of the raw data, which makes its performance highly dependent on the level of neighbouring data similarity. Also, for the first time we propose and demonstrate a BOTDA assisted by advanced Block-Matching and 3D filtering (BM3D) denoising technique, which minimizes the degradation of the two parameters even under higher SNR improvement and smaller SPN. BM3D takes the advantage of NLM and WD and utilizes the spatial-domain non-local principle to enhance the denoising in the transform domain, thus it shows the least degradation of measurement accuracy/experimental spatial resolution after denoising. Thus the BOTDA assisted by BM3D maintains the best measurement accuracy/experimental spatial resolution compared with those by NLM and WD. We also show that BM3D has the advantage of temperature independent performance, unlike NLM where the accuracy is affected by the temperature value. We believe BM3D would be an excellent denoising technique for state-of-the-art BOTDA sensors. In addition, this work is also valuable for practical applications of image denoising techniques in BOTDA sensors with respect to the appropriate choice of image denoising techniques, design of SNR improvement and the adoption of SPN to maintain optimal measurement accuracy/experimental spatial resolution/data acquisition speed.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2017 (3)

2016 (3)

M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration,” Nat. Commun. 7, 10870 (2016).
[Crossref] [PubMed]

Z. Zhao, M. A. Soto, M. Tang, and L. Thévenaz, “Distributed shape sensing using Brillouin scattering in multi-core fibers,” Opt. Express 24(22), 25211–25223 (2016).
[Crossref] [PubMed]

A. Denisov, M. A. Soto, and L. Thévenaz, “Going beyond 1000000 resolved points in a Brillouin distributed fiber sensor: theoretical analysis and experimental demonstration,” Light Sci. Appl. 5(5), 1–8 (2016).
[Crossref]

2014 (1)

L. Shao, R. Yan, X. Li, and Y. Liu, “From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms,” IEEE Trans. Cybern. 44(7), 1001–1013 (2014).
[Crossref] [PubMed]

2013 (1)

F. Argenti, A. Lapini, L. Alparone, and T. Bianchi, “A tutorial on speckle reduction in synthetic aperture radar images,” IEEE Geosci. Remote S. 1(3), 6–35 (2013).
[Crossref]

2012 (4)

2010 (1)

A. Motil, A. Bergman, and M. Tur, “State of the art of Brillouin fiber-optic distributed sensing,” Opt. Laser Technol. 78, 81–103 (2010).
[Crossref]

2009 (1)

V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola, “From Local Kernel to Nonlocal Multiple-Model Image Denoising,” Int. J. Comput. Vis. 86(1), 1–32 (2009).
[Crossref]

2008 (3)

2007 (1)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

1989 (1)

S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. 11(7), 674–693 (1989).
[Crossref]

Alparone, L.

F. Argenti, A. Lapini, L. Alparone, and T. Bianchi, “A tutorial on speckle reduction in synthetic aperture radar images,” IEEE Geosci. Remote S. 1(3), 6–35 (2013).
[Crossref]

Argenti, F.

F. Argenti, A. Lapini, L. Alparone, and T. Bianchi, “A tutorial on speckle reduction in synthetic aperture radar images,” IEEE Geosci. Remote S. 1(3), 6–35 (2013).
[Crossref]

Astola, J.

V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola, “From Local Kernel to Nonlocal Multiple-Model Image Denoising,” Int. J. Comput. Vis. 86(1), 1–32 (2009).
[Crossref]

Bao, X.

Bergman, A.

A. Motil, A. Bergman, and M. Tur, “State of the art of Brillouin fiber-optic distributed sensing,” Opt. Laser Technol. 78, 81–103 (2010).
[Crossref]

Bianchi, T.

F. Argenti, A. Lapini, L. Alparone, and T. Bianchi, “A tutorial on speckle reduction in synthetic aperture radar images,” IEEE Geosci. Remote S. 1(3), 6–35 (2013).
[Crossref]

Bolognini, G.

Brox, T.

T. Brox, O. Kleinschmidt, and D. Cremers, “Efficient nonlocal means for denoising of textural patterns,” IEEE Trans. Image Process. 17(7), 1083–1092 (2008).
[Crossref] [PubMed]

Buades, A.

A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Conference on Computer Vision and Pattern Recognition (CVPR), (IEEE, 2005), pp. 60–65.

Chen, L.

Coll, B.

A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Conference on Computer Vision and Pattern Recognition (CVPR), (IEEE, 2005), pp. 60–65.

Colpitts, B. G.

Cremers, D.

T. Brox, O. Kleinschmidt, and D. Cremers, “Efficient nonlocal means for denoising of textural patterns,” IEEE Trans. Image Process. 17(7), 1083–1092 (2008).
[Crossref] [PubMed]

Dabov, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

K. Dabov, A. Foi, and K. Egiazarian, “Video denoising by sparse 3D transform-domain collaborative filtering,” in Proc. European Signal Processing Conference (EUSIPCO) (IEEE, 2007), pp. 145–149.

Denisov, A.

A. Denisov, M. A. Soto, and L. Thévenaz, “Going beyond 1000000 resolved points in a Brillouin distributed fiber sensor: theoretical analysis and experimental demonstration,” Light Sci. Appl. 5(5), 1–8 (2016).
[Crossref]

Di Pasquale, F.

Egiazarian, K.

V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola, “From Local Kernel to Nonlocal Multiple-Model Image Denoising,” Int. J. Comput. Vis. 86(1), 1–32 (2009).
[Crossref]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

K. Dabov, A. Foi, and K. Egiazarian, “Video denoising by sparse 3D transform-domain collaborative filtering,” in Proc. European Signal Processing Conference (EUSIPCO) (IEEE, 2007), pp. 145–149.

Fang, L.

Farahani, M. A.

Farsiu, S.

Foi, A.

V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola, “From Local Kernel to Nonlocal Multiple-Model Image Denoising,” Int. J. Comput. Vis. 86(1), 1–32 (2009).
[Crossref]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

K. Dabov, A. Foi, and K. Egiazarian, “Video denoising by sparse 3D transform-domain collaborative filtering,” in Proc. European Signal Processing Conference (EUSIPCO) (IEEE, 2007), pp. 145–149.

Guerra, E. C.

Guo, N.

H. Wu, L. Wang, N. Guo, C. Shu, and C. Lu, “Brillouin Optical Time Domain Analyzer Assisted by Support Vector Machine for Ultrafast Temperature Extraction,” J. Lightwave Technol. 35(19), 4159–4167 (2017).
[Crossref]

N. Guo, L. Wang, H. Wu, C. Jin, H. Y. Tam, and C. Lu, “Enhanced Coherent BOTDA System without Trace Averaging,” J. Lightwave Technol., in press (2017).

He, Q.

Izatt, J. A.

Jia, X.

Jin, C.

N. Guo, L. Wang, H. Wu, C. Jin, H. Y. Tam, and C. Lu, “Enhanced Coherent BOTDA System without Trace Averaging,” J. Lightwave Technol., in press (2017).

Katkovnik, V.

V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola, “From Local Kernel to Nonlocal Multiple-Model Image Denoising,” Int. J. Comput. Vis. 86(1), 1–32 (2009).
[Crossref]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

Kleinschmidt, O.

T. Brox, O. Kleinschmidt, and D. Cremers, “Efficient nonlocal means for denoising of textural patterns,” IEEE Trans. Image Process. 17(7), 1083–1092 (2008).
[Crossref] [PubMed]

Lapini, A.

F. Argenti, A. Lapini, L. Alparone, and T. Bianchi, “A tutorial on speckle reduction in synthetic aperture radar images,” IEEE Geosci. Remote S. 1(3), 6–35 (2013).
[Crossref]

Lebrun, M.

M. Lebrun, “An analysis and implementation of the BM3D image denoising method,” Image Process. On Line 2, 175–213 (2012).
[Crossref]

Li, S.

Li, W.

Li, X.

L. Shao, R. Yan, X. Li, and Y. Liu, “From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms,” IEEE Trans. Cybern. 44(7), 1001–1013 (2014).
[Crossref] [PubMed]

Li, Y.

Liu, Y.

L. Shao, R. Yan, X. Li, and Y. Liu, “From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms,” IEEE Trans. Cybern. 44(7), 1001–1013 (2014).
[Crossref] [PubMed]

Lu, C.

H. Wu, L. Wang, N. Guo, C. Shu, and C. Lu, “Brillouin Optical Time Domain Analyzer Assisted by Support Vector Machine for Ultrafast Temperature Extraction,” J. Lightwave Technol. 35(19), 4159–4167 (2017).
[Crossref]

N. Guo, L. Wang, H. Wu, C. Jin, H. Y. Tam, and C. Lu, “Enhanced Coherent BOTDA System without Trace Averaging,” J. Lightwave Technol., in press (2017).

Mallat, S. G.

S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. 11(7), 674–693 (1989).
[Crossref]

Martins, D. L. N.

C. A. N. Santos, D. L. N. Martins, and N. D. A. Mascarenhas, “Ultrasound Image Despeckling Using Stochastic Distance-Based BM3D,” IEEE Trans. Image Process. 26(6), 2632–2643 (2017).
[Crossref] [PubMed]

Mascarenhas, N. D. A.

C. A. N. Santos, D. L. N. Martins, and N. D. A. Mascarenhas, “Ultrasound Image Despeckling Using Stochastic Distance-Based BM3D,” IEEE Trans. Image Process. 26(6), 2632–2643 (2017).
[Crossref] [PubMed]

Morel, J. M.

A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Conference on Computer Vision and Pattern Recognition (CVPR), (IEEE, 2005), pp. 60–65.

Motil, A.

A. Motil, A. Bergman, and M. Tur, “State of the art of Brillouin fiber-optic distributed sensing,” Opt. Laser Technol. 78, 81–103 (2010).
[Crossref]

Nie, Q.

Qian, X.

Ramírez, J. A.

M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration,” Nat. Commun. 7, 10870 (2016).
[Crossref] [PubMed]

M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Optimizing Image Denoising for Long-Range Brillouin Distributed Fiber Sensing,” J. Lightwave Technol., in press (2017).

Santos, C. A. N.

C. A. N. Santos, D. L. N. Martins, and N. D. A. Mascarenhas, “Ultrasound Image Despeckling Using Stochastic Distance-Based BM3D,” IEEE Trans. Image Process. 26(6), 2632–2643 (2017).
[Crossref] [PubMed]

Shao, L.

L. Shao, R. Yan, X. Li, and Y. Liu, “From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms,” IEEE Trans. Cybern. 44(7), 1001–1013 (2014).
[Crossref] [PubMed]

Shu, C.

Soto, M. A.

M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration,” Nat. Commun. 7, 10870 (2016).
[Crossref] [PubMed]

Z. Zhao, M. A. Soto, M. Tang, and L. Thévenaz, “Distributed shape sensing using Brillouin scattering in multi-core fibers,” Opt. Express 24(22), 25211–25223 (2016).
[Crossref] [PubMed]

A. Denisov, M. A. Soto, and L. Thévenaz, “Going beyond 1000000 resolved points in a Brillouin distributed fiber sensor: theoretical analysis and experimental demonstration,” Light Sci. Appl. 5(5), 1–8 (2016).
[Crossref]

M. A. Soto, G. Bolognini, and F. Di Pasquale, “Analysis of optical pulse coding in spontaneous Brillouin-based distributed temperature sensors,” Opt. Express 16(23), 19097–19111 (2008).
[Crossref] [PubMed]

M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Optimizing Image Denoising for Long-Range Brillouin Distributed Fiber Sensing,” J. Lightwave Technol., in press (2017).

Sun, W.

Tam, H. Y.

N. Guo, L. Wang, H. Wu, C. Jin, H. Y. Tam, and C. Lu, “Enhanced Coherent BOTDA System without Trace Averaging,” J. Lightwave Technol., in press (2017).

Tang, M.

Thévenaz, L.

Z. Zhao, M. A. Soto, M. Tang, and L. Thévenaz, “Distributed shape sensing using Brillouin scattering in multi-core fibers,” Opt. Express 24(22), 25211–25223 (2016).
[Crossref] [PubMed]

A. Denisov, M. A. Soto, and L. Thévenaz, “Going beyond 1000000 resolved points in a Brillouin distributed fiber sensor: theoretical analysis and experimental demonstration,” Light Sci. Appl. 5(5), 1–8 (2016).
[Crossref]

M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration,” Nat. Commun. 7, 10870 (2016).
[Crossref] [PubMed]

M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Optimizing Image Denoising for Long-Range Brillouin Distributed Fiber Sensing,” J. Lightwave Technol., in press (2017).

Toth, C. A.

Tur, M.

A. Motil, A. Bergman, and M. Tur, “State of the art of Brillouin fiber-optic distributed sensing,” Opt. Laser Technol. 78, 81–103 (2010).
[Crossref]

Wang, L.

H. Wu, L. Wang, N. Guo, C. Shu, and C. Lu, “Brillouin Optical Time Domain Analyzer Assisted by Support Vector Machine for Ultrafast Temperature Extraction,” J. Lightwave Technol. 35(19), 4159–4167 (2017).
[Crossref]

N. Guo, L. Wang, H. Wu, C. Jin, H. Y. Tam, and C. Lu, “Enhanced Coherent BOTDA System without Trace Averaging,” J. Lightwave Technol., in press (2017).

Wang, Z.

Wu, H.

Wylie, M. T. V.

Xue, N.

Yan, R.

L. Shao, R. Yan, X. Li, and Y. Liu, “From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms,” IEEE Trans. Cybern. 44(7), 1001–1013 (2014).
[Crossref] [PubMed]

Zhang, B.

Zhao, Z.

Appl. Opt. (1)

Biomed. Opt. Express (1)

IEEE Geosci. Remote S. (1)

F. Argenti, A. Lapini, L. Alparone, and T. Bianchi, “A tutorial on speckle reduction in synthetic aperture radar images,” IEEE Geosci. Remote S. 1(3), 6–35 (2013).
[Crossref]

IEEE Trans. Cybern. (1)

L. Shao, R. Yan, X. Li, and Y. Liu, “From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms,” IEEE Trans. Cybern. 44(7), 1001–1013 (2014).
[Crossref] [PubMed]

IEEE Trans. Image Process. (3)

C. A. N. Santos, D. L. N. Martins, and N. D. A. Mascarenhas, “Ultrasound Image Despeckling Using Stochastic Distance-Based BM3D,” IEEE Trans. Image Process. 26(6), 2632–2643 (2017).
[Crossref] [PubMed]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

T. Brox, O. Kleinschmidt, and D. Cremers, “Efficient nonlocal means for denoising of textural patterns,” IEEE Trans. Image Process. 17(7), 1083–1092 (2008).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. (1)

S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. 11(7), 674–693 (1989).
[Crossref]

Image Process. On Line (1)

M. Lebrun, “An analysis and implementation of the BM3D image denoising method,” Image Process. On Line 2, 175–213 (2012).
[Crossref]

Int. J. Comput. Vis. (1)

V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola, “From Local Kernel to Nonlocal Multiple-Model Image Denoising,” Int. J. Comput. Vis. 86(1), 1–32 (2009).
[Crossref]

J. Lightwave Technol. (2)

Light Sci. Appl. (1)

A. Denisov, M. A. Soto, and L. Thévenaz, “Going beyond 1000000 resolved points in a Brillouin distributed fiber sensor: theoretical analysis and experimental demonstration,” Light Sci. Appl. 5(5), 1–8 (2016).
[Crossref]

Nat. Commun. (1)

M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration,” Nat. Commun. 7, 10870 (2016).
[Crossref] [PubMed]

Opt. Express (3)

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Figures (9)

Fig. 1
Fig. 1 (a) Measured BGS distribution along FUT without denoising; SNR evolution along FUT for different levels of SNR improvements by (b) NLM and (c) WD; measured BGS and temperature distribution along FUT under SNR improvement of 13dB by (d, f) NLM and (e, g) WD. Inset: zoom-in view around the heated sections. The sampling rate is 500MSample/s, corresponding to SPN = 10 within 2m section.
Fig. 2
Fig. 2 Temperature distribution around the 2m heated section at the location of 62km with (a1)-(a3) NLM and (b1)-(b3) WD for denoising when SPNs are 10, 6, and 4 within 2m section, respectively. The 2m section is heated to 65°C.
Fig. 3
Fig. 3 Temperature distribution around the last 200m heated section with (a1)-(a3) NLM and (b1)-(b3) WD for denoising when SPNs are 10, 6, and 4 within 2m section, respectively. The 200m section is heated to 45°C.
Fig. 4
Fig. 4 (a) SNR evolution along FUT for different levels of SNR improvements by BM3D; measured (b) BGS and (c) temperature distribution along FUT under SNR improvement of 13dB. Inset: zoom-in view around the heated sections. The sampling rate is 500 MSample/s, corresponding to SPN = 10 within 2m section.
Fig. 5
Fig. 5 Temperature distribution around the 2m heated section at the location of 62km with BM3D for denoising when SPNs are (a) 10, (b) 6, and (c) 4 within 2m section, respectively. The 2m section is heated to 65°C.
Fig. 6
Fig. 6 Temperature distribution around the last 200m heated section with BM3D for denoising when SPNs are (a) 10, (b) 6, and (c) 4 within 2m section, respectively. The 200m section is heated to 45°C.
Fig. 7
Fig. 7 Temperature degradation as a function of SNR improvement by NLM, WD and BM3D when SPNs are (a) 10, (b) 6, and (c) 4 within 2m section, respectively. The absolute error of temperature is calculated using the results in Fig. 2 for NLM and WD and Fig. 5 for BM3D, respectively. The 2m section is heated to 65°C.
Fig. 8
Fig. 8 Degradation of experimental spatial resolution as a function of SNR improvement by NLM, WD and BM3D when SPNs are (a) 10, (b) 6, and (c) 4 within 2m section, respectively. The absolute error of spatial resolution is calculated using the results in Fig. 3 for NLM and WD and Fig. 6 for BM3D, respectively. The 200m section is heated to 45°C.
Fig. 9
Fig. 9 Measured temperature versus the oven temperature when SPNs are (a) 10, (b) 6, and (c) 4 within 2m section, respectively. The 2m section at the location of 62km is heated to 35°C, 45 °C, 55 °C, 65 °C and 75 °C, respectively. The SNR improvement is the same 13dB after denoising by NLM, WD and BM3D, respectively.

Equations (3)

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  f i = j N i w i,j *g( x j ) j N i w i,j
  w i,j = G h ( g i g j 2 )=exp{ 1 h 2 g i g j 2 }
τ(x)={ x if| x |>λ 0 if| x |λ

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