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Impact of Underwater Image Enhancement on Feature Matching
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Mark Jones ,
Catherine Seale
Sensors, Volume: 25, Issue: 22, Start page: 6966
Swansea University Authors:
Jason Summers , Mark Jones
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© 2025 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
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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...
| Published in: | Sensors |
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| ISSN: | 1424-8220 |
| Published: |
MDPI AG
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70881 |
| 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 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. |
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| Keywords: |
underwater image enhancement; SLAM; feature matching; furthest matching frame; feature robustness; context-aware benchmarking |
| College: |
Faculty of Science and Engineering |
| Funders: |
UKRI (EP/S021892/1) |
| Issue: |
22 |
| Start Page: |
6966 |