UIE-UnFold：结合颜色先验与视觉Transformer的深度展开网络用于水下图像增强
UIE-UnFold: Deep Unfolding Network with Color Priors and Vision Transformer for Underwater Image Enhancement
AI总结：
提出一种结合颜色先验和视觉Transformer的深度展开网络，用于水下图像增强，通过显式建模物理过程提升准确性和可靠性，并在多个数据集上超越现有方法。
摘要：
水下图像增强（UIE）在各类海洋应用中发挥着至关重要的作用，但由于复杂的水下环境，该任务仍然具有挑战性。当前基于学习的方法常常缺乏对水下图像形成所涉及的物理过程的先验知识的显式整合，尽管其增强效果令人印象深刻，但优化能力有限。本文提出了一种新颖的用于UIE的深度展开网络（DUN），该网络整合了颜色先验和阶段间特征变换以提升增强性能。所提出的DUN模型将基于模型的方法的迭代优化和可靠性与深度学习的灵活性和表征能力相结合，与现有的基于学习的UIE方法相比，提供了更具可解释性和稳定性的解决方案。所提出的模型由三个关键组件组成：一个颜色先验引导模块（CPGB），用于建立退化图像和原始图像颜色通道之间的映射；一个非线性激活梯度下降模块（NAGDM），用于模拟水下图像退化过程；以及一个阶段间特征变换器（ISF-Former），用于促进不同网络阶段之间的特征交换。通过显式整合颜色先验并建模水下图像形成的物理特性，所提出的DUN模型实现了更准确和可靠的增强结果。在多个水下图像数据集上进行的大量实验表明，所提出的模型在定量和定性评估中均优于最先进的方法。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，能够在海洋研究中实现更准确和可靠的科学分析。代码可在https://github.com/CXH-Research/UIE-UnFold获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
