GPR Image Noise Removal Using Grey Wolf Optimisation in the NSST Domain

Xingkun He,Rongyao Zheng,Xiwen Li,Can Wang

doi:10.3390/rs13214416

Xingkun He, Rongyao Zheng + Show 2 more

Open Access

https://doi.org/10.3390/rs13214416

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Journal: Remote sensing	Publication Date: Nov 2, 2021
Citations: 5	License type: CC BY 4.0

Affiliation: Huazhong University of Science and Technology

Abstract

Hyper-wavelet transforms, such as a non-subsampled shearlet transform (NSST), are one of the mainstream algorithms for removing random noise from ground-penetrating radar (GPR) images. Because GPR image noise is non-uniform, the use of a single fixed threshold for noisy coefficients in each sub-band of hyper-wavelet denoising algorithms is not appropriate. To overcome this problem, a novel NSST-based GPR image denoising grey wolf optimisation (GWO) algorithm is proposed. First, a time-varying threshold function based on the trend of noise changes in GPR images is proposed. Second, an edge area recognition and protection method based on the Canny algorithm is proposed. Finally, GWO is employed to select appropriate parameters for the time-varying threshold function and edge area protection method. The Natural Image Quality Evaluator is utilised as the optimisation index. The experiment results demonstrate that the proposed method provides excellent noise removal performance while protecting edge signals.

Highlights

Ground-penetrating radar (GPR) is a valuable instrument that uses high-frequency electromagnetic waves for geophysical detection of buried objects [1,2]
This study proposes a novel grey wolf optimisation (GWO) framework for GPR image denoising based on the non-subsampled shearlet transform (NSST) domain
The proposed timevarying threshold function makes the denoising threshold conform to the trend of noise change in GPR images

Summary

Introduction

Ground-penetrating radar (GPR) is a valuable instrument that uses high-frequency electromagnetic waves for geophysical detection of buried objects [1,2]. It provides highresolution, non-destructive, and intuitive results and is widely used in subgradequality inspection [3], archaeological excavation [4], environmental protection [5], buildingquality inspection [6], military applications [7], and other fields. Owing to signal attenuation and geometrical spreading losses in GPR signals received from great depths under the ground, a time-varying gain is used to enhance the signals. Random noise removal is an important research topic in GPR image processing. [9,10,11]

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