Lightweight Dual-Antagonistic Underwater Image Enhancement Network: A High-Performance Real-Time Approach Combining Knowledge Distillation

Underwater optical imaging, known for its high resolution and rich color information, is widely used in underwater exploration and monitoring. However, it is affected by light absorption and scattering, leading to color attenuation, blurring, and low contrast, which impact visual tasks. Meanwhile, the development of underwater applications towards mobile platforms poses challenges for the efficient deployment of models due to limited computational resources. To address these challenges, this paper proposes a lightweight underwater image enhancement network, WDGA L (Water DOOC GAN Air Light), which integrates human visual dual-opponent characteristics with knowledge distillation. Inspired by human color constancy, the network is designed with a dual-opponent mechanism to enhance color restoration and detail clarity, thereby improving adaptability. Additionally, a knowledge distillation strategy is incorporated, introducing fog density feature imitation loss and adaptive local self-feature distillation loss. These components enable the student network to significantly reduce parameter size while maintaining enhancement performance and improving computational efficiency. The proposed lightweight model is successfully deployed on the RK3588 platform, achieving 32.8 FPS for real-time underwater image observation. Experimental results demonstrate that, compared to state-of-the-art (SOTA) methods, WDGA L improves UCIQE and UIQM metrics by 23.7%, verifying its practicality and feasibility in resource constrained underwater environments. WDGA L shows promising applications in marine intelligent devices, offering an efficient and lightweight solution for underwater image enhancement.