Correlation of Image Quality Metrics with Expert Perception for Industrial Computed Tomography
This research investigates the effectiveness of image quality metrics in predicting human perception of superresolution computed tomography images. By comparing multiple metrics and correlating their scores with human expert ratings on image attributes such as noise, contrast, and sharpness, we aim...
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Format: | Article |
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NDT.net
2025-02-01
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Series: | e-Journal of Nondestructive Testing |
Online Access: | https://www.ndt.net/search/docs.php3?id=30753 |
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author | Lukas Behammer Patrick Weinberger Lukas Nepelius Bernhard Fröhler Miroslav Yosifov Johann Kastner Sascha Senck |
author_facet | Lukas Behammer Patrick Weinberger Lukas Nepelius Bernhard Fröhler Miroslav Yosifov Johann Kastner Sascha Senck |
author_sort | Lukas Behammer |
collection | DOAJ |
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This research investigates the effectiveness of image quality metrics in predicting human perception of superresolution computed tomography images. By comparing multiple metrics and correlating their scores with human expert ratings on image attributes such as noise, contrast, and sharpness, we aim to identify metrics that closely align with human visual judgment. Our findings highlight the limitations of relying solely on a single metric for image quality assessment and emphasize the importance of considering specific tasks and image content when selecting appropriate metrics. This study contributes to a deeper understanding of the relationship between objective image quality metrics and subjective human perception in the context of industrial computed tomography.
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format | Article |
id | doaj-art-064ee899ce614d7da0a17dd942395ae6 |
institution | Kabale University |
issn | 1435-4934 |
language | deu |
publishDate | 2025-02-01 |
publisher | NDT.net |
record_format | Article |
series | e-Journal of Nondestructive Testing |
spelling | doaj-art-064ee899ce614d7da0a17dd942395ae62025-02-06T10:48:19ZdeuNDT.nete-Journal of Nondestructive Testing1435-49342025-02-0130210.58286/30753Correlation of Image Quality Metrics with Expert Perception for Industrial Computed TomographyLukas BehammerPatrick WeinbergerLukas NepeliusBernhard Fröhlerhttps://orcid.org/0000-0003-1271-0838Miroslav Yosifovhttps://orcid.org/0000-0002-6318-4869Johann Kastnerhttps://orcid.org/0000-0002-3163-0916Sascha Senckhttps://orcid.org/0000-0001-8400-5121 This research investigates the effectiveness of image quality metrics in predicting human perception of superresolution computed tomography images. By comparing multiple metrics and correlating their scores with human expert ratings on image attributes such as noise, contrast, and sharpness, we aim to identify metrics that closely align with human visual judgment. Our findings highlight the limitations of relying solely on a single metric for image quality assessment and emphasize the importance of considering specific tasks and image content when selecting appropriate metrics. This study contributes to a deeper understanding of the relationship between objective image quality metrics and subjective human perception in the context of industrial computed tomography. https://www.ndt.net/search/docs.php3?id=30753 |
spellingShingle | Lukas Behammer Patrick Weinberger Lukas Nepelius Bernhard Fröhler Miroslav Yosifov Johann Kastner Sascha Senck Correlation of Image Quality Metrics with Expert Perception for Industrial Computed Tomography e-Journal of Nondestructive Testing |
title | Correlation of Image Quality Metrics with Expert Perception for Industrial Computed Tomography |
title_full | Correlation of Image Quality Metrics with Expert Perception for Industrial Computed Tomography |
title_fullStr | Correlation of Image Quality Metrics with Expert Perception for Industrial Computed Tomography |
title_full_unstemmed | Correlation of Image Quality Metrics with Expert Perception for Industrial Computed Tomography |
title_short | Correlation of Image Quality Metrics with Expert Perception for Industrial Computed Tomography |
title_sort | correlation of image quality metrics with expert perception for industrial computed tomography |
url | https://www.ndt.net/search/docs.php3?id=30753 |
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