Underwater image enhancement (UIE) plays a crucial role in various marine applications, but it remains challenging due to the complex underwater environment. Current learning-based approaches frequently lack explicit incorporation of prior knowledge about the physical processes involved in underwater image formation, resulting in limited optimization despite their impressive enhancement results. This paper proposes a novel deep unfolding network (DUN) for UIE that integrates color priors and inter-stage feature transformation to improve enhancement performance. The proposed DUN model combines the iterative optimization and reliability of model-based methods with the flexibility and representational power of deep learning, offering a more explainable and stable solution compared to existing learning-based UIE approaches. The proposed model consists of three key components: a Color Prior Guidance Block (CPGB) that establishes a mapping between color channels of degraded and original images, a Nonlinear Activation Gradient Descent Module (NAGDM) that simulates the underwater image degradation process, and an Inter Stage Feature Transformer (ISF-Former) that facilitates feature exchange between different network stages. By explicitly incorporating color priors and modeling the physical characteristics of underwater image formation, the proposed DUN model achieves more accurate and reliable enhancement results. Extensive experiments on multiple underwater image datasets demonstrate the superiority of the proposed model over state-of-the-art methods in both quantitative and qualitative evaluations. The proposed DUN-based approach offers a promising solution for UIE, enabling more accurate and reliable scientific analysis in marine research. The code is available at https://github.com/CXH-Research/UIE-UnFold.

翻译：水下图像增强（UIE）在各类海洋应用中发挥着关键作用，但由于水下环境的复杂性，该任务仍具挑战性。当前基于学习的方法通常未能显式地融入水下图像形成物理过程的先验知识，导致尽管其增强效果显著，但优化能力有限。本文提出一种新颖的用于UIE的深度展开网络（DUN），该网络通过整合色彩先验与阶段间特征变换来提升增强性能。所提出的DUN模型结合了基于模型方法的迭代优化可靠性与深度学习的灵活性及表征能力，相比现有的基于学习的UIE方法，提供了更具可解释性和稳定性的解决方案。该模型包含三个关键组件：建立退化图像与原始图像色彩通道间映射的色彩先验引导块（CPGB）、模拟水下图像退化过程的非线性激活梯度下降模块（NAGDM），以及促进不同网络阶段间特征交换的阶段间特征变换器（ISF-Former）。通过显式地融入色彩先验并对水下图像形成的物理特性进行建模，所提出的DUN模型实现了更准确可靠的增强结果。在多个水下图像数据集上的大量实验表明，该模型在定量与定性评估上均优于现有先进方法。所提出的基于DUN的方法为UIE提供了一种有前景的解决方案，有助于在海洋研究中实现更准确可靠的科学分析。代码公开于 https://github.com/CXH-Research/UIE-UnFold。