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Impact of Underwater Image Enhancement on Feature Matching

Jason M. Summers Orcid Logo, Mark Jones Orcid Logo, Catherine Seale Orcid Logo, JASON SUMMERS

Sensors, Volume: 25, Issue: 22, Start page: 6966

Swansea University Authors: Mark Jones Orcid Logo, JASON SUMMERS

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DOI (Published version): 10.3390/s25226966

Abstract

We introduce local matching stability and furthest matchable frame as quantitative measures for evaluating the success of underwater image enhancement. This enhancement process addresses visual degradation caused by light absorption, scattering, marine growth, and debris. Enhanced imagery plays a cr...

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Published in: Sensors
ISSN: 1424-8220
Published: MDPI AG 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa70881
first_indexed 2025-11-12T16:01:31Z
last_indexed 2025-11-15T14:38:25Z
id cronfa70881
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spelling 2025-11-14T16:12:24.9224506 v2 70881 2025-11-12 Impact of Underwater Image Enhancement on Feature Matching 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false 07e278c85fd0619f7f40c3391dc4ff37 JASON SUMMERS JASON SUMMERS true false 2025-11-12 MACS We introduce local matching stability and furthest matchable frame as quantitative measures for evaluating the success of underwater image enhancement. This enhancement process addresses visual degradation caused by light absorption, scattering, marine growth, and debris. Enhanced imagery plays a critical role in downstream tasks such as path detection and autonomous navigation for underwater vehicles, relying on robust feature extraction and frame matching.To assess the impact of enhancement techniques on frame-matching performance, we propose a novel evaluation framework tailored to underwater environments. Through metric-based analysis, we identify strengths and limitations of existing approaches and pinpoint gaps in their assessment of real-world applicability. By incorporating a practical matching strategy, our framework offers a robust, context-aware benchmark for comparing enhancement methods.Finally, we demonstrate how visual improvements affect the performance of a complete real-world algorithm -- Simultaneous Localization and Mapping (SLAM) -- reinforcing the framework's relevance to operational underwater scenarios. Journal Article Sensors 25 22 6966 MDPI AG 1424-8220 14 11 2025 2025-11-14 10.3390/s25226966 https://doi.org/10.3390/s25226966 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) UKRI EP/S021892/1 2025-11-14T16:12:24.9224506 2025-11-12T09:17:17.9071742 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Jason M. Summers 0009-0006-4161-2865 1 Mark Jones 0000-0001-8991-1190 2 Catherine Seale 0000-0002-7107-3958 3 JASON SUMMERS 4
title Impact of Underwater Image Enhancement on Feature Matching
spellingShingle Impact of Underwater Image Enhancement on Feature Matching
Mark Jones
JASON SUMMERS
title_short Impact of Underwater Image Enhancement on Feature Matching
title_full Impact of Underwater Image Enhancement on Feature Matching
title_fullStr Impact of Underwater Image Enhancement on Feature Matching
title_full_unstemmed Impact of Underwater Image Enhancement on Feature Matching
title_sort Impact of Underwater Image Enhancement on Feature Matching
author_id_str_mv 2e1030b6e14fc9debd5d5ae7cc335562
07e278c85fd0619f7f40c3391dc4ff37
author_id_fullname_str_mv 2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones
07e278c85fd0619f7f40c3391dc4ff37_***_JASON SUMMERS
author Mark Jones
JASON SUMMERS
author2 Jason M. Summers
Mark Jones
Catherine Seale
JASON SUMMERS
format Journal article
container_title Sensors
container_volume 25
container_issue 22
container_start_page 6966
publishDate 2025
institution Swansea University
issn 1424-8220
doi_str_mv 10.3390/s25226966
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url https://doi.org/10.3390/s25226966
document_store_str 0
active_str 0
description We introduce local matching stability and furthest matchable frame as quantitative measures for evaluating the success of underwater image enhancement. This enhancement process addresses visual degradation caused by light absorption, scattering, marine growth, and debris. Enhanced imagery plays a critical role in downstream tasks such as path detection and autonomous navigation for underwater vehicles, relying on robust feature extraction and frame matching.To assess the impact of enhancement techniques on frame-matching performance, we propose a novel evaluation framework tailored to underwater environments. Through metric-based analysis, we identify strengths and limitations of existing approaches and pinpoint gaps in their assessment of real-world applicability. By incorporating a practical matching strategy, our framework offers a robust, context-aware benchmark for comparing enhancement methods.Finally, we demonstrate how visual improvements affect the performance of a complete real-world algorithm -- Simultaneous Localization and Mapping (SLAM) -- reinforcing the framework's relevance to operational underwater scenarios.
published_date 2025-11-14T05:31:54Z
_version_ 1851098099612647424
score 11.444473

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