Wavelet Decomposition Layer Selection for the <i>φ</i>-OTDR Signal
The choice of wavelet decomposition layer (DL) not only affects the denoising quality of wavelet denoising (WD), but also limits the denoising efficiency, especially when dealing with real phase-sensitive optical time-domain reflectometry (<inline-formula><math xmlns="http://www.w3.org...
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MDPI AG
2024-01-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/11/2/137 |
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| author | Yunfei Chen Kaimin Yu Minfeng Wu Lei Feng Yuanfang Zhang Peibin Zhu Wen Chen Jianzhong Hao |
| author_facet | Yunfei Chen Kaimin Yu Minfeng Wu Lei Feng Yuanfang Zhang Peibin Zhu Wen Chen Jianzhong Hao |
| author_sort | Yunfei Chen |
| collection | DOAJ |
| description | The choice of wavelet decomposition layer (DL) not only affects the denoising quality of wavelet denoising (WD), but also limits the denoising efficiency, especially when dealing with real phase-sensitive optical time-domain reflectometry (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>φ</mi></semantics></math></inline-formula>-OTDR) signals with complex signal characteristics and different noise distributions. In this paper, a straightforward adaptive DL selection method is introduced, which dose not require known noise and clean signals, but relies on the similarity between the probability density function (PDF) of method noise (MN) and the PDF of Gaussian white noise. Validation is carried out using hypothetical noise signals and measured <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>φ</mi></semantics></math></inline-formula>-OTDR vibration signals by comparison with conventional metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The proposed wavelet DL selection method contributes to the fast processing of distributed fiber optic sensing signals and further improves the system performance. |
| first_indexed | 2024-03-07T22:16:35Z |
| format | Article |
| id | doaj.art-17d7b54bdaa847758566d5037f927f61 |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-07T22:16:35Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj.art-17d7b54bdaa847758566d5037f927f612024-02-23T15:31:38ZengMDPI AGPhotonics2304-67322024-01-0111213710.3390/photonics11020137Wavelet Decomposition Layer Selection for the <i>φ</i>-OTDR SignalYunfei Chen0Kaimin Yu1Minfeng Wu2Lei Feng3Yuanfang Zhang4Peibin Zhu5Wen Chen6Jianzhong Hao7School of Ocean Information Engineering, Jimei University, Xiamen 361021, ChinaSchool of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen 361021, ChinaSchool of Electrical Engineering and Artificial Intelligence, Xiamen University Malaysia, Sepang 43900, MalaysiaSchool of Ocean Information Engineering, Jimei University, Xiamen 361021, ChinaSchool of Ocean Information Engineering, Jimei University, Xiamen 361021, ChinaSchool of Ocean Information Engineering, Jimei University, Xiamen 361021, ChinaSchool of Ocean Information Engineering, Jimei University, Xiamen 361021, ChinaInstitute for Infocomm Research (I<sup>2</sup>R), Agency for Science, Technology and Research (A⋆STAR), Singapore 138632, SingaporeThe choice of wavelet decomposition layer (DL) not only affects the denoising quality of wavelet denoising (WD), but also limits the denoising efficiency, especially when dealing with real phase-sensitive optical time-domain reflectometry (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>φ</mi></semantics></math></inline-formula>-OTDR) signals with complex signal characteristics and different noise distributions. In this paper, a straightforward adaptive DL selection method is introduced, which dose not require known noise and clean signals, but relies on the similarity between the probability density function (PDF) of method noise (MN) and the PDF of Gaussian white noise. Validation is carried out using hypothetical noise signals and measured <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>φ</mi></semantics></math></inline-formula>-OTDR vibration signals by comparison with conventional metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The proposed wavelet DL selection method contributes to the fast processing of distributed fiber optic sensing signals and further improves the system performance.https://www.mdpi.com/2304-6732/11/2/137method noiseimage processingfiber optic sensoroptimizationwavelet transform |
| spellingShingle | Yunfei Chen Kaimin Yu Minfeng Wu Lei Feng Yuanfang Zhang Peibin Zhu Wen Chen Jianzhong Hao Wavelet Decomposition Layer Selection for the <i>φ</i>-OTDR Signal Photonics method noise image processing fiber optic sensor optimization wavelet transform |
| title | Wavelet Decomposition Layer Selection for the <i>φ</i>-OTDR Signal |
| title_full | Wavelet Decomposition Layer Selection for the <i>φ</i>-OTDR Signal |
| title_fullStr | Wavelet Decomposition Layer Selection for the <i>φ</i>-OTDR Signal |
| title_full_unstemmed | Wavelet Decomposition Layer Selection for the <i>φ</i>-OTDR Signal |
| title_short | Wavelet Decomposition Layer Selection for the <i>φ</i>-OTDR Signal |
| title_sort | wavelet decomposition layer selection for the i φ i otdr signal |
| topic | method noise image processing fiber optic sensor optimization wavelet transform |
| url | https://www.mdpi.com/2304-6732/11/2/137 |
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