Self-Denoising of BOTDA Using Deep Convolutional Neural Networks
We propose the self-denoising network (SDNet), a self-supervised network based on a convolutional neural network (CNN), for Brillouin trace denoising. With the target noisy image as the only input, the proposed method has no hardware restriction, requirement for image priors, or assumption for noise...
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IEEE
2025-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10972307/ |
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| author | Di Qi Chun-Kit Chan Xun Guan |
| author_facet | Di Qi Chun-Kit Chan Xun Guan |
| author_sort | Di Qi |
| collection | DOAJ |
| description | We propose the self-denoising network (SDNet), a self-supervised network based on a convolutional neural network (CNN), for Brillouin trace denoising. With the target noisy image as the only input, the proposed method has no hardware restriction, requirement for image priors, or assumption for noise distribution. The Bernoulli mask and the partial convolutional layer implemented in the encoding process help capture the input features efficiently, while the dropout in the decoding process extends the feasibility and generality of the proposed network. Experimental results indicate that for Brillouin traces with frequency steps of 0.5 MHz/1 MHz, the proposed SDNet can improve the accuracy of Brillouin frequency shift (BFS) estimation by 40%/61%, 32%/31% and 24%/32% under input signal-to-noise ratios (SNR) of 5.5 dB, 8.5 dB, and 11.5 dB, respectively, without degradation in spatial resolution. Furthermore, the SDNet demonstrates a robust denoising performance in BOTDA systems with different application scenarios. |
| format | Article |
| id | doaj-art-c75501d9004d4b1ea93d759e357f64f7 |
| institution | OA Journals |
| issn | 1943-0655 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-c75501d9004d4b1ea93d759e357f64f72025-08-20T01:48:14ZengIEEEIEEE Photonics Journal1943-06552025-01-0117311010.1109/JPHOT.2025.356340510972307Self-Denoising of BOTDA Using Deep Convolutional Neural NetworksDi Qi0Chun-Kit Chan1https://orcid.org/0000-0002-7046-5335Xun Guan2https://orcid.org/0000-0001-5045-6004Lightwave Communications Laboratory, Department of Information Engineering, The Chinese University of Hong Kong, Hong KongLightwave Communications Laboratory, Department of Information Engineering, The Chinese University of Hong Kong, Hong KongTsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, ChinaWe propose the self-denoising network (SDNet), a self-supervised network based on a convolutional neural network (CNN), for Brillouin trace denoising. With the target noisy image as the only input, the proposed method has no hardware restriction, requirement for image priors, or assumption for noise distribution. The Bernoulli mask and the partial convolutional layer implemented in the encoding process help capture the input features efficiently, while the dropout in the decoding process extends the feasibility and generality of the proposed network. Experimental results indicate that for Brillouin traces with frequency steps of 0.5 MHz/1 MHz, the proposed SDNet can improve the accuracy of Brillouin frequency shift (BFS) estimation by 40%/61%, 32%/31% and 24%/32% under input signal-to-noise ratios (SNR) of 5.5 dB, 8.5 dB, and 11.5 dB, respectively, without degradation in spatial resolution. Furthermore, the SDNet demonstrates a robust denoising performance in BOTDA systems with different application scenarios.https://ieeexplore.ieee.org/document/10972307/BOTDAsingle-image denoisingself-supervised learningSNR enhancementpartial convolutional neural network (CNN) |
| spellingShingle | Di Qi Chun-Kit Chan Xun Guan Self-Denoising of BOTDA Using Deep Convolutional Neural Networks IEEE Photonics Journal BOTDA single-image denoising self-supervised learning SNR enhancement partial convolutional neural network (CNN) |
| title | Self-Denoising of BOTDA Using Deep Convolutional Neural Networks |
| title_full | Self-Denoising of BOTDA Using Deep Convolutional Neural Networks |
| title_fullStr | Self-Denoising of BOTDA Using Deep Convolutional Neural Networks |
| title_full_unstemmed | Self-Denoising of BOTDA Using Deep Convolutional Neural Networks |
| title_short | Self-Denoising of BOTDA Using Deep Convolutional Neural Networks |
| title_sort | self denoising of botda using deep convolutional neural networks |
| topic | BOTDA single-image denoising self-supervised learning SNR enhancement partial convolutional neural network (CNN) |
| url | https://ieeexplore.ieee.org/document/10972307/ |
| work_keys_str_mv | AT diqi selfdenoisingofbotdausingdeepconvolutionalneuralnetworks AT chunkitchan selfdenoisingofbotdausingdeepconvolutionalneuralnetworks AT xunguan selfdenoisingofbotdausingdeepconvolutionalneuralnetworks |