See Through Water: Heuristic Modeling Toward Color Correction for Underwater Image Enhancement

Color cast is one of the main degradations in underwater images. Existing data-driven methods, while capable of learning color correction rules from large datasets, often overlook the imaging characteristics and light behavior in underwater environments, making them unable to accurately restore colors in complex water bodies. To address this, we use color constancy and an underwater imaging model to heuristically model the underwater environment for accurate color restoration. On one hand, we propose a multi-scale joint prior network architecture to fully explore the rich feature-level information at different scales in underwater images. This is used to fit the complex parameters of the underwater imaging model, deriving high-quality potential undegraded images. On the other hand, to tackle the challenges of color distortion caused by complex imaging factors in different water environments, we estimate the background light of the water body through the color constancy of underwater objects and dynamically incorporate it into the underwater imaging model as a prior. This not only guides the learning process more effectively but also allows the model to consider key aspects of underwater optical propagation, making it adaptable to different water environments and improving the color accuracy of the enhanced images. We have also conducted extensive experiments to demonstrate the effectiveness of the proposed method, which not only achieves the best overall performance in qualitative analysis and quantitative comparison but also boasts the best color accuracy and the fastest inference speed. The code is available at https://github.com/JunyuFan/MJPNet.