提出新评估框架，量化水下图像增强对特征匹配的影响。
Impact of Underwater Image Enhancement on Feature Matching
- 引入局部匹配稳定性与最远可匹配帧作为量化指标
- 验证增强后图像显著提升特征匹配成功率
- 适合水下机器人导航与SLAM系统开发者参考
我们引入局部匹配稳定性和最远可匹配帧作为量化指标，评估水下图像增强的效果。该增强过程解决光吸收、散射、海洋生物附着和碎屑引起的视觉退化问题。增强后的图像在路径检测和自主导航等下游任务中至关重要，依赖于鲁棒的特征提取与帧匹配。为评估增强技术对帧匹配性能的影响，我们提出一种专为水下环境设计的新评估框架。通过基于度量的分析，我们揭示了现有方法的优势与局限，并指出现有评估在真实场景适用性上的不足。通过引入实用匹配策略，该框架提供了一个鲁棒且上下文感知的基准，可用于比较不同增强方法。最后，我们展示了视觉改善如何影响完整实际算法——同时定位与地图构建（SLAM）——的表现，验证了该框架在真实水下应用场景中的相关性。
原文摘要 · Abstract (English)
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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