Real-Time Denoising of Brillouin Optical Time Domain Analyzer With High Data Fidelity Using Convolutional Neural Networks

In recent years, many conventional image denoising techniques have been intensively studied to enhance the signal to noise ratio (SNR) of Brillouin optical time domain analyzer (BOTDA), due to their superior denoising performance to one-dimensional methods. However, in the case of low sampling rate, the details of the signal are smoothed out due to less useful information, resulting in a degradation of the spatial resolution. Moreover, these conventional denoising algorithms are quite time-consuming compared with the BOTDA measuring time. To overcome these drawbacks, we employ a feed-forward convolutional neural networks (CNN) based image denoising for BOTDA. A conventional BOTDA system with 15 ns pulse width is implemented to demonstrate the effectiveness of the exploited CNN-based denoising method. The actual electrical noise signals of the BOTDA at different sampling rates are collected to synthesize training samples. The CNN model is trained with the noise and simulated BOTDA signals. Experimental results show that SNR improvement of 13.43 dB, 13.57 dB, and 12.9 dB is achieved at a sampling rate of 500 MSa/s, 250 MSa/s, and 125 MSa/s, respectively, via the trained CNN denoiser. No spatial resolution distortion can be observed in the denoised BOTDA signals. Besides, the CNN denoiser only takes 0.045 s to process a 151 × 50000 image benefiting from GPU computing. This processing time is negligible compared with the acquisition time of BOTDA, which makes real-time denoising possible.