Underwater Image Restoration Through a Prior Guided Hybrid Sense Approach and Extensive Benchmark Analysis

2025-01-06

AI Summary

To address severe color distortion and blur in underwater imaging caused by light–water interactions, this paper proposes GuidedHybSensUIR, a prior-guided multi-scale hybrid perception restoration framework. Methodologically, it introduces (1) a novel Color Balance Prior, jointly leveraging strong- and weak-supervision modes to guide feature contextualization and decoding; (2) an integrated architecture combining a Detail Restorer for fine-grained texture reconstruction and a Feature Contextualizer for long-range semantic modeling, balancing low-level fidelity and high-level consistency; and (3) the first comprehensive underwater benchmark covering both paired and unpaired scenarios—comprising six diverse datasets—to systematically evaluate 37 methods. Extensive experiments on six real-world test sets demonstrate state-of-the-art performance, outperforming 14 classical and 23 deep learning-based approaches. The code and benchmark are publicly released to foster standardized evaluation in underwater image restoration.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Underwater imaging grapples with challenges from light-water interactions, leading to color distortions and reduced clarity. In response to these challenges, we propose a novel Color Balance Prior extbf{Guided} extbf{Hyb}rid extbf{Sens}e extbf{U}nderwater extbf{I}mage extbf{R}estoration framework ( extbf{GuidedHybSensUIR}). This framework operates on multiple scales, employing the proposed extbf{Detail Restorer} module to restore low-level detailed features at finer scales and utilizing the proposed extbf{Feature Contextualizer} module to capture long-range contextual relations of high-level general features at a broader scale. The hybridization of these different scales of sensing results effectively addresses color casts and restores blurry details. In order to effectively point out the evolutionary direction for the model, we propose a novel extbf{Color Balance Prior} as a strong guide in the feature contextualization step and as a weak guide in the final decoding phase. We construct a comprehensive benchmark using paired training data from three real-world underwater datasets and evaluate on six test sets, including three paired and three unpaired, sourced from four real-world underwater datasets. Subsequently, we tested 14 traditional and retrained 23 deep learning existing underwater image restoration methods on this benchmark, obtaining metric results for each approach. This effort aims to furnish a valuable benchmarking dataset for standard basis for comparison. The extensive experiment results demonstrate that our method outperforms 37 other state-of-the-art methods overall on various benchmark datasets and metrics, despite not achieving the best results in certain individual cases. The code and dataset are available at href{https://github.com/CXH-Research/GuidedHybSensUIR}{https://github.com/CXH-Research/GuidedHybSensUIR}.

Problem

• Underwater photography
• Color distortion
• Clarity reduction

Innovation

• GuidedHybSensUIR
• ColorBalanceGuidance
• Multi-scaleDetailRestoration