Impact of Underwater Image Enhancement on Feature Matching

2025-07-29

AI Summary

Underwater images suffer severe degradation due to light absorption, scattering, biofouling, and suspended particulates. This work systematically evaluates how image enhancement affects feature matching performance—a critical prerequisite for visual navigation and SLAM. We propose two task-oriented, quantitative metrics—“local matching stability” and “farthest matchable frame”—to establish the first context-aware evaluation framework tailored to downstream vision tasks such as autonomous navigation and SLAM. Through comprehensive feature matching analysis, metric-based assessment, and end-to-end SLAM validation, we expose a significant performance gap between mainstream enhancement methods and real-world task requirements. Experiments demonstrate that our framework more accurately reflects the practical improvement in trajectory estimation and pose robustness conferred by enhancement, outperforming conventional distortion- or perception-based evaluation paradigms.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: State Estimation
• Search and Optimization: Local Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

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.

Problem

• Evaluates underwater image enhancement impact on feature matching
• Addresses visual degradation from light absorption and scattering
• Assesses enhancement effects on SLAM algorithm performance

Innovation

• Proposes novel evaluation framework for underwater enhancement
• Uses local matching stability for quantitative assessment
• Incorporates practical matching strategy for robust benchmarking