Work

Self-Denoising of BOTDA Using Deep Convolutional Neural Networks

Year: 2025

Type: article

Abstract: We propose the self-denoising network (SDNet), a self-supervised network based on a convolutional neural network (CNN), for Brillouin trace denoising. With the target noisy image as the only input, the proposed method has no hardware restriction, requirement for image priors, or assumption for noise distribution. The Bernoulli mask and the partial convolutional layer implemented in the encoding process help capture the input features efficiently, while the dropout in the decoding process extends the feasibility and generality of the proposed network. Experimental results indicate that for Brillouin traces with frequency steps of 0.5 MHz/1 MHz, the proposed SDNet can improve the accuracy of Brillouin frequency shift (BFS) estimation by 40%/61%, 32%/31% and 24%/32% under input signal-to-noise ratios (SNR) of 5.5 dB, 8.5 dB, and 11.5 dB, respectively, without degradation in spatial resolution. Furthermore, the SDNet demonstrates a robust denoising performance in BOTDA systems with different application scenarios.

Source: IEEE photonics journal

DOI: 10.1109/jphot.2025.3563405

Authors: Di Qi, Chun-Kit Chan, Xun Guan

Institutions: Chinese University of Hong Kong, Tsinghua Shenzhen International Graduate School, Tsinghua University

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funder: National Natural Science Foundation of China