🤖 AI Summary
This work addresses the challenges of low visibility and color distortion in underwater images caused by light scattering and absorption by proposing a variational model-based deep unfolding network. For the first time, the Mamba architecture is introduced into underwater image enhancement, leveraging a dehazing decomposition, multiplicative residual terms, and non-local gradient constraints to model scene self-similarity. A proximal trajectory loss is specifically designed to ensure consistency between the unfolding process and ideal regularized iterations. The proposed framework offers both theoretical guarantees on solution existence and strong capability in fine detail recovery, significantly outperforming state-of-the-art methods in both visual quality and quantitative metrics, thereby effectively enhancing image clarity and color fidelity.
📝 Abstract
Underwater imaging plays a crucial role in ocean engineering, although captured data often suffer from poor visibility and color distortion. To address these challenges, we propose a model-based deep unfolding network for underwater image enhancement that integrates variational modeling into a learnable architecture. The framework is guided by a variational formulation based on a dehazing decomposition, incorporating a multiplicative residual component to absorb remaining artifacts and a nonlocal gradient-type constraint to preserve structural details and enhance edge sharpness. We provide a theoretical analysis establishing the existence of solution for the associated minimization problem. The proposed unfolding method incorporates Mamba layers to efficiently capture self-similarities in the scene. In addition, we introduce a proximal trajectory loss that enforces consistency between the unfolding stages and the iterations of an ideal restoration regularizer. Experimental results demonstrate that the proposed unfolding approach achieves improved visual quality and competitive quantitative performance compared with recent state-of-the-art methods. The source code will be available at https://github.com/MIA-UIB/Variational-Unfolding-Mamba-Underwater-Enhancement .