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

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.

Figures (14)

• Figure 1: Comparison of different methods in the FLOPs–PSNR space on UIEB dataset. The x-axis denotes FLOPs (G, 256 × 256 inputs), y-axis indicates PSNR (dB), and circle size is used to indicate parameter count.
• Figure 2: Overview of our proposed UIE framework. The pipeline begins with the AWCC module, which adaptively compensates degraded color channels and stabilizes luminance through secondary correction. The enhanced image is then processed by a CNN backbone incorporating MRDConv to capture multi-directional features with efficient inference. Finally, the SGCA module leverages global statistical priors to refine color temperature, tint, and saturation, producing high-fidelity enhancement with low computational overhead.
• Figure 3: Visualization of intermediate values for AWCC and the SGCA modules: The red and blue channels are visualized using the AUTUMN and WINTER colormaps, respectively.
• Figure 4: Architecture of the MRDConv and the Re-parameterization process.
• Figure 5: Comparison of visual performance across different methods on five full-reference benchmark datasets.