Open AccessRapid noise removal based dual adversarial network for the Brillouin optical time domain analyzer
Author(s) -
Jingyang Liu,
Tao Wang,
Qian Zhang,
Huan Chen,
Jianzhong Zhang,
Lingling Qiao,
Shaohua Gao,
Mingjiang Zhang
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.436032
Subject(s) - computer science , noise (video) , time domain , signal to noise ratio (imaging) , a priori and a posteriori , optics , maximum a posteriori estimation , algorithm , noise reduction , optical fiber , artificial intelligence , image (mathematics) , computer vision , mathematics , physics , telecommunications , statistics , maximum likelihood , philosophy , epistemology
We propose a dual adversarial network (DANet) to improve the signal-to-noise ratio (SNR) of the Brillouin optical time domain analyzer. Rather than inferring the conditional posteriori distribution in the conventional maximum a posteriori (MAP) framework, DANet constructs a joint distribution from two different factorizations corresponding to the noise removal and generation tasks. This method utilizes all the information between the clean-noisy image pairs to preserve data completely without requiring traditional image priors and noise distribution assumptions. Additionally, the clean-noisy image pairs produced by the generator can expand the original dataset to retrain and enhance the denoising effect. The performance of DANet is verified using the simulated and experimental data. Without spatial resolution deterioration, an SNR improvement of 35.51 dB is observed in the simulation, and the Brillouin frequency shift (BFS) uncertainty along the fiber is reduced by 3.56 MHz. Experiments yield a maximum SNR improvement of 19.08 dB, with the BFS uncertainty along the fiber reduced by 0.93 MHz. Significantly, DANet has a processing time of 1.26 s, which is considerably faster than conventional methods, demonstrating its potential for rapid noise removal tasks.