Liu Jingyang, Wang Tao, Zhang Qian, Chen Huan, Zhang Jianzhong, Qiao Lijun, Gao Shaohua, Zhang Mingjiang
Opt Express. 2021 Oct 11;29(21):34002-34014. doi: 10.1364/OE.436032.
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.
我们提出了一种双对抗网络（DANet）来提高布里渊光时域分析仪的信噪比（SNR）。DANet并非在传统的最大后验概率（MAP）框架中推断条件后验分布，而是从对应于噪声去除和生成任务的两种不同因式分解中构建联合分布。该方法利用了干净图像与噪声图像对之间的所有信息，无需传统图像先验知识和噪声分布假设即可完全保留数据。此外，生成器产生的干净图像与噪声图像对可以扩展原始数据集以进行重新训练并增强去噪效果。使用模拟数据和实验数据验证了DANet的性能。在不降低空间分辨率的情况下，模拟中观察到信噪比提高了35.51 dB，沿光纤的布里渊频移（BFS）不确定性降低了3.56 MHz。实验中最大信噪比提高了19.08 dB，沿光纤的BFS不确定性降低了0.93 MHz。值得注意的是，DANet的处理时间为1.26 s，比传统方法快得多，证明了其在快速噪声去除任务中的潜力。
