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A Dilated Residual Network for Turbine Blade ICT Image Artifact Removal

Artifacts are divergent strip artifacts or dark stripe artifacts in Industrial Computed Tomography (ICT) images due to large differences in density among the components of scanned objects, which can significantly distort the actual structure of scanned objects in ICT images. The presence of artifact...

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Detalles Bibliográficos
Autores principales: Han, Rui, Zeng, Fengying, Li, Jing, Yao, Zhenwen, Guo, Wenhua, Zhao, Jiyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9866201/
https://www.ncbi.nlm.nih.gov/pubmed/36679825
http://dx.doi.org/10.3390/s23021028
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author Han, Rui
Zeng, Fengying
Li, Jing
Yao, Zhenwen
Guo, Wenhua
Zhao, Jiyuan
author_facet Han, Rui
Zeng, Fengying
Li, Jing
Yao, Zhenwen
Guo, Wenhua
Zhao, Jiyuan
author_sort Han, Rui
collection PubMed
description Artifacts are divergent strip artifacts or dark stripe artifacts in Industrial Computed Tomography (ICT) images due to large differences in density among the components of scanned objects, which can significantly distort the actual structure of scanned objects in ICT images. The presence of artifacts can seriously affect the practical application effectiveness of ICT in defect detection and dimensional measurement. In this paper, a series of convolution neural network models are designed and implemented based on preparing the ICT image artifact removal datasets. Our findings indicate that the RF (receptive field) and the spatial resolution of network can significantly impact the effectiveness of artifact removal. Therefore, we propose a dilated residual network for turbine blade ICT image artifact removal (DRAR), which enhances the RF of the network while maintaining spatial resolution with only a slight increase in computational load. Extensive experiments demonstrate that the DRAR achieves exceptional performance in artifact removal.
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spelling pubmed-98662012023-01-22 A Dilated Residual Network for Turbine Blade ICT Image Artifact Removal Han, Rui Zeng, Fengying Li, Jing Yao, Zhenwen Guo, Wenhua Zhao, Jiyuan Sensors (Basel) Article Artifacts are divergent strip artifacts or dark stripe artifacts in Industrial Computed Tomography (ICT) images due to large differences in density among the components of scanned objects, which can significantly distort the actual structure of scanned objects in ICT images. The presence of artifacts can seriously affect the practical application effectiveness of ICT in defect detection and dimensional measurement. In this paper, a series of convolution neural network models are designed and implemented based on preparing the ICT image artifact removal datasets. Our findings indicate that the RF (receptive field) and the spatial resolution of network can significantly impact the effectiveness of artifact removal. Therefore, we propose a dilated residual network for turbine blade ICT image artifact removal (DRAR), which enhances the RF of the network while maintaining spatial resolution with only a slight increase in computational load. Extensive experiments demonstrate that the DRAR achieves exceptional performance in artifact removal. MDPI 2023-01-16 /pmc/articles/PMC9866201/ /pubmed/36679825 http://dx.doi.org/10.3390/s23021028 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Han, Rui
Zeng, Fengying
Li, Jing
Yao, Zhenwen
Guo, Wenhua
Zhao, Jiyuan
A Dilated Residual Network for Turbine Blade ICT Image Artifact Removal
title A Dilated Residual Network for Turbine Blade ICT Image Artifact Removal
title_full A Dilated Residual Network for Turbine Blade ICT Image Artifact Removal
title_fullStr A Dilated Residual Network for Turbine Blade ICT Image Artifact Removal
title_full_unstemmed A Dilated Residual Network for Turbine Blade ICT Image Artifact Removal
title_short A Dilated Residual Network for Turbine Blade ICT Image Artifact Removal
title_sort dilated residual network for turbine blade ict image artifact removal
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9866201/
https://www.ncbi.nlm.nih.gov/pubmed/36679825
http://dx.doi.org/10.3390/s23021028
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