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.
Abstract (translated)
我们引入局部匹配稳定性和最远可匹配帧作为评估水下图像增强成功与否的量化指标。该增强过程旨在解决由光吸收、散射、海洋生物生长和垃圾引起的视觉退化问题。经过增强的图像在下游任务中起着关键作用，例如路径检测和自主导航，这些任务依赖于稳健的功能提取和帧匹配。为了评估增强技术对帧匹配性能的影响，我们提出了一种专为水下环境设计的新颖评价框架。通过基于指标的分析，我们识别现有方法的优势与局限，并指出它们在评估实际应用性方面存在的差距。通过纳入实用的匹配策略，我们的框架提供了一个稳健且上下文感知的基准，用于比较增强方法的效果。最后，我们展示了视觉改进如何影响一个完整的现实世界算法——同时定位和地图构建（SLAM）——的表现，从而强调了该框架在操作水下场景中的相关性。
URL
https://arxiv.org/abs/2507.21715
