Paper
11 September 2024 Denoising of underground pipeline data by ground-penetrating radar based on GPR-DUNet
Jiahao Li, Huiqin Wang
Author Affiliations +
Proceedings Volume 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024); 132531X (2024) https://doi.org/10.1117/12.3041592
Event: 4th International Conference on Signal Image Processing and Communication, 2024, Xi'an, China
Abstract
The presence of noise can seriously affect the intelligent decoding and recognition of Ground-Penetrating Radar (GPR) underground pipelines, in view of this, this paper proposes a deep unfolding network (GPR-DUNet) suitable for denoising GPR underground pipeline data. Firstly, in the gradient descender part, a gated gradient descent module is designed to unfold the proximal gradient descent algorithm, which enables the network to be trained even when the degradation matrix is unknown, secondly, in the denoiser part, a denoising proximity mapping module is constructed to obtain features at different scales of the GPR image using the group normalized channel attention mechanism and a simplified local enhanced feed-forward network to dramatically improve the denoising performance, and lastly, a cross-stage feature fusion submodule was designed to address the problem of information loss between stages. The experimental results on real and simulated GPR image denoising show that the method has good results in the field of GPR image denoising.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Jiahao Li and Huiqin Wang "Denoising of underground pipeline data by ground-penetrating radar based on GPR-DUNet", Proc. SPIE 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024), 132531X (11 September 2024); https://doi.org/10.1117/12.3041592
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Denoising

General packet radio service

Image denoising

Ground penetrating radar

Feature extraction

Image processing

Deep learning

Back to Top