Yuan Pei, Lu Yuangang, Zhang Yuyang, Zhang Zelin
Appl Opt. 2022 Apr 1;61(10):2667-2674. doi: 10.1364/AO.449195.
A novel frequency-domain image processing method is proposed, to the best of our knowledge, to filter the noise from data collected by distributed optical fiber sensors based on Brillouin optical time-domain sensing (BOTDS). In the proposed method, we first divide a data image into subimages, and then we filter the noisy subimages by retaining the useful frequency information corresponding to the Lorentz-shape frequency spectrum and Brillouin frequency shift (BFS) transitions. The denoising performance improvements are verified by simulation and experiment. The performances in terms of temperature/strain measurement uncertainty, spatial resolution, and processing time achieved by the proposed filter are then compared with those by using a Gaussian filter and a nonlocal means (NLM) filter. In a proof-of-concept experiment with a 5.2 km length G657 sensing fiber, we achieve a temperature measurement uncertainty improvement of 27% compared with the results obtained by using the Gaussian filtering method. Furthermore, the processing speed of the proposed method is 22 times faster than that of the NLM filter under the same temperature measurement uncertainty.
据我们所知，提出了一种新颖的频域图像处理方法，用于对基于布里渊光时域传感（BOTDS）的分布式光纤传感器收集的数据进行噪声滤波。在所提出的方法中，我们首先将数据图像划分为子图像，然后通过保留与洛伦兹形状频谱和布里渊频移（BFS）跃迁相对应的有用频率信息来对有噪声的子图像进行滤波。通过仿真和实验验证了去噪性能的提升。然后将所提出的滤波器在温度/应变测量不确定度、空间分辨率和处理时间方面的性能与使用高斯滤波器和非局部均值（NLM）滤波器的性能进行比较。在一个使用5.2公里长的G657传感光纤的概念验证实验中，与使用高斯滤波方法获得的结果相比，我们实现了温度测量不确定度27%的提升。此外，在相同温度测量不确定度下，所提出方法的处理速度比NLM滤波器快22倍。
