New BFS Retrieval Technique for Brillouin Optical Time Domain Analysis Sensor System

Haoyu Wei,Xiangjun Xin,Qiming Wang,Xiya Lu,Chao Li,Yongjun Wang,Hongxin Wu,Lei Fan

doi:10.3390/electronics10111334

Haoyu Wei, Xiangjun Xin + Show 6 more

Open Access

https://doi.org/10.3390/electronics10111334

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Abstract

In this paper, Gaussian smoothing (GS), non-local means (NLM), and Quaternion Wavelet Transform (QWT) have been described in detail. Furthermore, a Brillouin optical time domain analysis (BOTDA) experimental system was built to verify the denoising algorithms. The principal and experimental analyses show that the QWT algorithm is a more efficient image denoising method. The results indicate that the GS algorithm can obtain the highest signal-to-noise ratio (SNR), frequency uncertainty, and Brillouin frequency shift (BFS) accuracy, and can be executed in an imperceptible time, but the GS algorithm has the lowest spatial resolution. After being denoised by using NLM algorithm, although SNR, frequency uncertainty, BFS accuracy, and spatial resolution significantly improved, it takes 40 min to implement the NLM denoising algorithm for a BGS image with 200 × 100,000 points. Processed by the QWT denoising algorithm, although SNR increases to 17.26 dB and frequency uncertainty decreases to 0.24 MHz, a BFS accuracy of only 0.2 MHz can be achieved. Moreover, the spatial resolution is 3 m, which is not affected by the QWT denoising algorithm. It takes less than 32 s to denoise the same raw BGS data. The QWT image denoising technique is suitable for BGS data processing in the BOTDA sensor system.

Highlights

The Brillouin gain spectrum (BGS) image processed by denoising algorithms is mapped from raw BGS data which comes from a Brillouin optical time domain analysis (BOTDA) experimental system with approximately 40 km long sensing fiber under test (FUT) shown in Figure 1a,b, which shows the physical picture of our experimental system
The results indicate that the Brillouin frequency shift (BFS) distributions fluctuate within ±2.5 MHz under the condition of the actual temperature 44 ◦ C, 55 ◦ C, 64 ◦ C, and 75 ◦ C when BFSs are retrieved by Lorentz curve fitting (LCF) with raw BGS
We describe in detail the principles of Gaussian smoothing (GS), non-local means (NLM), and Quaternion Wavelet Transform (QWT) image denoising algorithms, and propose these algorithms to denoise BGS images

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In order to meet the requirements of high precision and high spatial resolution real-time measurement, one of researchers’ main tasks is focused on finding some highly time-efficient methods to improve the signal-to-noise ratio (SNR) of BGS. With the development of digital signal processing technology, many digital denoising methods, such as wavelet denoising (WD) [11] and the lifting wavelet transform (LWT) [12], have been used for BGS denoising and can be implemented with high time efficiency, these traditional algorithms have limited denoising effects. In order to enhance the time efficiency, improve measurement accuracy, and eliminate the degradation of spatial resolution from the image blurring, our research team proposed applying a Quaternion Wavelet Transform (QWT) image denoising algorithm to BGS image processing. The research results of this paper have a certain guiding role for the application of QWT image denoising algorithm technique in BOTDA sensor system

BGS Image Denoising Algorithms

GS Image Denoising Algorithm

Non-Local Means Image Denoising Algorithm

Quaternion Wavelet Transform Image Denoising Algorithm

Discussion

Conclusions