Impact of Underwater Image Enhancement on Feature Matching

TL;DR

This paper proposes a new evaluation framework for underwater image enhancement that measures its impact on feature matching and SLAM performance, addressing the challenges of visual degradation in underwater environments.

Contribution

It introduces local matching stability and furthest matchable frame as quantitative metrics, providing a novel, practical benchmark for assessing enhancement techniques in underwater vision tasks.

Findings

Enhanced images improve feature matching robustness

Evaluation framework reveals strengths and limitations of existing methods

Visual improvements positively impact underwater SLAM performance

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.