ROTDR signal enhancement via deep convolutional denoising autoencoders trained with domain randomization

I. Laarossi; A. Pardo Franco; O. M. Conde; M. A. Quintela; José Miguel López-Higuera

doi:10.1117/12.2540012

14 October 2019 ROTDR signal enhancement via deep convolutional denoising autoencoders trained with domain randomization

I. Laarossi, A. Pardo Franco, O. M. Conde, M. A. Quintela, José Miguel López-Higuera

Author Affiliations +

Proceedings Volume 11199, Seventh European Workshop on Optical Fibre Sensors; 111993N (2019) https://doi.org/10.1117/12.2540012
Event: Seventh European Workshop on Optical Fibre Sensors, 2019, Limassol, Cyprus

Abstract

In this work, a deep convolutional adaptive filter is proposed to enhance the performance of a Raman based distributed temperature sensor system by the application of domain randomization methods for its training. The improvement of the signal-to-noise ratio in the Raman backscattered signals in the training process and translation to a real scenario is demonstrated. The ability of the proposed technique to reduce signal noise effectively is proved independently of the sensor configuration and without degradation of temperature accuracy or spatial resolution of these systems. Moreover, using single trace to noise reduction in the ROTDR signals accelerates the system response avoiding the employment of many averages in a unique measurement

Citation Download Citation

I. Laarossi, A. Pardo Franco, O. M. Conde, M. A. Quintela, and José Miguel López-Higuera "ROTDR signal enhancement via deep convolutional denoising autoencoders trained with domain randomization", Proc. SPIE 11199, Seventh European Workshop on Optical Fibre Sensors, 111993N (14 October 2019); https://doi.org/10.1117/12.2540012

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