Accelerating Brillouin fiber sensing via destructive-interference-enabled precise raw data acquisition and nonredundant image denoising

Abstract

Distributed Brillouin fiber sensing, based on the linear relationship between Brillouin frequency shift (BFS) and physical quantities applied to sensing fibers, has found numerous applications in the past few decades. Recently, various advanced image denoising methods have been used for performance enhancements in Brillouin fiber sensors. Yet, even though these methods do significantly remove noises contained in raw data, the BFS measurement uncertainty is not reduced–the newly introduced image denoising appears redundant with the conventional signal processing. Here, in order to truly make Brillouin fiber sensing benefit from image denoising, we directly map BFS from the image-denoised data via the slope-assisted analysis of the Brillouin phase-gain ratio. As such, noise reduction resulting from image denoising fully translates into measurement uncertainty reduction. In order to further optimize the performance of image-denoising-enhanced Brillouin fiber sensing, we improve the quality of the raw Brillouin gain and phase data by designing an advanced coherent detection scheme called a microwave-photonic interferometer, which converts some amplitude and phase noises into common-mode noises and further eliminates them through destructive interference. A more than 20-fold sensing speed acceleration compared to the state-of-the-art is experimentally achieved. This remarkable performance enhancement is obtained by only optimizing the signal detection and processing unit, without modifying Brillouin scattering between pump and probe waves. Our method seamlessly connects Brillouin fiber sensing with advanced image denoising methods developed for computer vision and artificial intelligence, and makes image-denoising-enhanced Brillouin fiber sensing outperform the state-of-the art significantly.