Flexible and high-gain DOFS deconvolution based on data-driven denoising prior

• Published in: Journal of lightwave technology 2023-10, Vol.41 (19), p.1-9
• Main Authors: Wu, Hao, Zhang, Mingming, Ge, Zhao, Luo, Zhongyao, Zhao, Can, Tang, Ming
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Convolution; convolutional neural networks; Deconvolution; Fiber optics sensors; High gain; Iterative methods; Machine learning; Neural networks; Noise reduction; Optical fibers; optical time domain reflectometry; Parameters; Spatial resolution; Training
• ISSN: 0733-8724; 1558-2213
• DOI: 10.1109/JLT.2023.3279465

Spatial resolution is an essential parameter for distributed optical fiber sensing (DOFS). Deconvolution is a promising solution to improve spatial resolution because of its good universality and ability to restore infinitely high spatial resolution in theory. However, conventional iterative deconvolution algorithms require manually designed priors, which makes it difficult to restore the signal accurately. Although end-to-end methods based on artificial neural networks provide more accurate results based on data-driven priors, a neural network can only be applied to data with similar characteristics to its training data. For systems with different parameters, the neural network needs to be retrained or fine-tuned, which is time and computational resource consuming. Here, we employ a new deconvolution method for DOFS based on half-quadratic splitting and denoising neural networks that exploits the advantages of both iterative and machine learning methods. Besides, we propose a method to calculate the deconvolution gain to evaluate the deconvolution performance quantitatively. In an experiment with 10 deconvolution ratios, the deconvolution gain of the employed method is 15.8 dB, while that of a conventional iterative method and an end-to-end machine learning method are 7.9 dB and 15 dB, respectively. Moreover, this new deconvolution method can be adapted to data with arbitrary system parameters, making it more flexible than end-to-end neural networks in practical applications.