Advancing Visual Reliability: Color-Accurate Underwater Image Enhancement for Real-Time Underwater Missions

2026-03-17

AI Summary

This work addresses the longstanding challenge in underwater image enhancement of simultaneously achieving real-time performance and high color fidelity, as existing methods either rely on computationally complex models that are difficult to deploy or lightweight architectures that underperform under severe degradation. To overcome this trade-off, the authors propose an efficient real-time enhancement framework that innovatively integrates adaptive red-blue channel compensation, multi-branch reparameterized dilated convolutions, and global color correction guided by statistical priors. Remarkably, the model operates with only 3,880 inference parameters, achieving a throughput of 409 FPS. It outperforms state-of-the-art methods across seven evaluation metrics on eight benchmark datasets, yielding a 29.7% improvement in UCIQE scores, and has been successfully deployed on an ROV platform, significantly enhancing downstream visual task performance.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Hardware-aware ML
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Underwater image enhancement plays a crucial role in providing reliable visual information for underwater platforms, since strong absorption and scattering in water-related environments generally lead to image quality degradation. Existing high-performance methods often rely on complex architectures, which hinder deployment on underwater devices. Lightweight methods often sacrifice quality for speed and struggle to handle severely degraded underwater images. To address this limitation, we present a real-time underwater image enhancement framework with accurate color restoration. First, an Adaptive Weighted Channel Compensation module is introduced to achieve dynamic color recovery of the red and blue channels using the green channel as a reference anchor. Second, we design a Multi-branch Re-parameterized Dilated Convolution that employs multi-branch fusion during training and structural re-parameterization during inference, enabling large receptive field representation with low computational overhead. Finally, a Statistical Global Color Adjustment module is employed to optimize overall color performance based on statistical priors. Extensive experiments on eight datasets demonstrate that the proposed method achieves state-of-the-art performance across seven evaluation metrics. The model contains only 3,880 inference parameters and achieves an inference speed of 409 FPS. Our method improves the UCIQE score by 29.7% under diverse environmental conditions, and the deployment on ROV platforms and performance gains in downstream tasks further validate its superiority for real-time underwater missions.

Problem

• underwater image enhancement
• real-time processing
• color restoration
• lightweight model
• image degradation

Innovation

• Color-Accurate Enhancement
• Real-Time Underwater Imaging
• Re-parameterized Convolution
• Adaptive Channel Compensation
• Lightweight Architecture