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

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...

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Autores principales:	Xingkun He, Can Wang, Rongyao Zheng, Xiwen Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	GPR image denoising grey wolf optimisation non-subsampled shearlet transform threshold function Science Q
Acceso en línea:	https://doaj.org/article/49369f7eac084983a72749f85e32ef09
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id	oai:doaj.org-article:49369f7eac084983a72749f85e32ef09
record_format	dspace
spelling	oai:doaj.org-article:49369f7eac084983a72749f85e32ef092021-11-11T18:55:56ZGPR Image Noise Removal Using Grey Wolf Optimisation in the NSST Domain10.3390/rs132144162072-4292https://doaj.org/article/49369f7eac084983a72749f85e32ef092021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4416https://doaj.org/toc/2072-4292Hyper-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.Xingkun HeCan WangRongyao ZhengXiwen LiMDPI AGarticleGPRimage denoisinggrey wolf optimisationnon-subsampled shearlet transformthreshold functionScienceQENRemote Sensing, Vol 13, Iss 4416, p 4416 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	GPR image denoising grey wolf optimisation non-subsampled shearlet transform threshold function Science Q
spellingShingle	GPR image denoising grey wolf optimisation non-subsampled shearlet transform threshold function Science Q Xingkun He Can Wang Rongyao Zheng Xiwen Li GPR Image Noise Removal Using Grey Wolf Optimisation in the NSST Domain
description	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.
format	article
author	Xingkun He Can Wang Rongyao Zheng Xiwen Li
author_facet	Xingkun He Can Wang Rongyao Zheng Xiwen Li
author_sort	Xingkun He
title	GPR Image Noise Removal Using Grey Wolf Optimisation in the NSST Domain
title_short	GPR Image Noise Removal Using Grey Wolf Optimisation in the NSST Domain
title_full	GPR Image Noise Removal Using Grey Wolf Optimisation in the NSST Domain
title_fullStr	GPR Image Noise Removal Using Grey Wolf Optimisation in the NSST Domain
title_full_unstemmed	GPR Image Noise Removal Using Grey Wolf Optimisation in the NSST Domain
title_sort	gpr image noise removal using grey wolf optimisation in the nsst domain
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/49369f7eac084983a72749f85e32ef09
work_keys_str_mv	AT xingkunhe gprimagenoiseremovalusinggreywolfoptimisationinthensstdomain AT canwang gprimagenoiseremovalusinggreywolfoptimisationinthensstdomain AT rongyaozheng gprimagenoiseremovalusinggreywolfoptimisationinthensstdomain AT xiwenli gprimagenoiseremovalusinggreywolfoptimisationinthensstdomain
_version_	1718431643000111104

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

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