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Sparse Blind Deconvolution with Nonconvex Optimization for Ultrasonic NDT Application

In the field of ultrasonic nondestructive testing (NDT), robust and accurate detection of defects is a challenging task because of the attenuation and noising of the ultrasonic wave from the structure. For determining the reflection characteristics representing the position and amplitude of ultrason...

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Detalles Bibliográficos
Autores principales: Gao, Xuyang, Shi, Yibing, Du, Kai, Zhu, Qi, Zhang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730569/
https://www.ncbi.nlm.nih.gov/pubmed/33291739
http://dx.doi.org/10.3390/s20236946
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author Gao, Xuyang
Shi, Yibing
Du, Kai
Zhu, Qi
Zhang, Wei
author_facet Gao, Xuyang
Shi, Yibing
Du, Kai
Zhu, Qi
Zhang, Wei
author_sort Gao, Xuyang
collection PubMed
description In the field of ultrasonic nondestructive testing (NDT), robust and accurate detection of defects is a challenging task because of the attenuation and noising of the ultrasonic wave from the structure. For determining the reflection characteristics representing the position and amplitude of ultrasonic detection signals, sparse blind deconvolution methods have been implemented to separate overlapping echoes when the ultrasonic transducer impulse response is unknown. This letter introduces the [Formula: see text] ratio regularization function to model the deconvolution as a nonconvex optimization problem. The initialization influences the accuracy of estimation and, for this purpose, the alternating direction method of multipliers (ADMM) combined with blind gain calibration is used to find the initial approximation to the real solution, given multiple observations in a joint sparsity case. The proximal alternating linearized minimization (PALM) algorithm is embedded in the iterate solution, in which the majorize-minimize (MM) approach accelerates convergence. Compared with conventional blind deconvolution algorithms, the proposed methods demonstrate the robustness and capability of separating overlapping echoes in the context of synthetic experiments.
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spelling pubmed-77305692020-12-12 Sparse Blind Deconvolution with Nonconvex Optimization for Ultrasonic NDT Application Gao, Xuyang Shi, Yibing Du, Kai Zhu, Qi Zhang, Wei Sensors (Basel) Letter In the field of ultrasonic nondestructive testing (NDT), robust and accurate detection of defects is a challenging task because of the attenuation and noising of the ultrasonic wave from the structure. For determining the reflection characteristics representing the position and amplitude of ultrasonic detection signals, sparse blind deconvolution methods have been implemented to separate overlapping echoes when the ultrasonic transducer impulse response is unknown. This letter introduces the [Formula: see text] ratio regularization function to model the deconvolution as a nonconvex optimization problem. The initialization influences the accuracy of estimation and, for this purpose, the alternating direction method of multipliers (ADMM) combined with blind gain calibration is used to find the initial approximation to the real solution, given multiple observations in a joint sparsity case. The proximal alternating linearized minimization (PALM) algorithm is embedded in the iterate solution, in which the majorize-minimize (MM) approach accelerates convergence. Compared with conventional blind deconvolution algorithms, the proposed methods demonstrate the robustness and capability of separating overlapping echoes in the context of synthetic experiments. MDPI 2020-12-04 /pmc/articles/PMC7730569/ /pubmed/33291739 http://dx.doi.org/10.3390/s20236946 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Letter
Gao, Xuyang
Shi, Yibing
Du, Kai
Zhu, Qi
Zhang, Wei
Sparse Blind Deconvolution with Nonconvex Optimization for Ultrasonic NDT Application
title Sparse Blind Deconvolution with Nonconvex Optimization for Ultrasonic NDT Application
title_full Sparse Blind Deconvolution with Nonconvex Optimization for Ultrasonic NDT Application
title_fullStr Sparse Blind Deconvolution with Nonconvex Optimization for Ultrasonic NDT Application
title_full_unstemmed Sparse Blind Deconvolution with Nonconvex Optimization for Ultrasonic NDT Application
title_short Sparse Blind Deconvolution with Nonconvex Optimization for Ultrasonic NDT Application
title_sort sparse blind deconvolution with nonconvex optimization for ultrasonic ndt application
topic Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730569/
https://www.ncbi.nlm.nih.gov/pubmed/33291739
http://dx.doi.org/10.3390/s20236946
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