针对水下图像多种退化共存问题，提出分区域协同修复框架。
CoRe-UIE: Rethinking Coexisting and Region-wise Degradation for Underwater Image Enhancement
- 设计分区域路由专家网络，按退化类型分配不同修复任务。
- 在三个数据集上实现优于现有方法的视觉与定量效果。
- 适合需要精细处理复杂水下退化的图像增强场景。
水下图像常受色彩失真、散射雾、纹理衰减和光照不均等多种退化影响，且这些退化在空间上共存且分布不均，传统统一修复方法难以适应。为此，我们提出面向退化的专家协作框架 CoRe-UIE，融合一个保持内容的共享专家与四个共享主干的路由专家，分别负责颜色校正、散射抑制、纹理恢复和光照保护。路由专家共享相同结构但参数独立，通过输入生成的退化线索与自适应Top-k路由机制分配至不同区域。进一步引入基于希尔伯特-施密特独立性准则（HSIC）的表征约束，降低专家特征间的统计相关性，缓解冗余响应。在UIEB、LSUI和U45数据集上的实验表明，CoRe-UIE在多样水下退化条件下均实现了具有竞争力的定量性能与视觉平衡的增强效果。
原文摘要 · Abstract (English)
Underwater images often suffer from diverse and coexisting degradations, including color distortion, scattering haze, texture attenuation, and uneven illumination. These degradations vary across regions and may coexist locally, making conventional uniform restoration difficult to adapt to different degradation patterns. To address this problem, we propose Coexisting and Region-wise Degradation for Underwater Image Enhancement (\textbf{CoRe-UIE}), a degradation-oriented expert collaboration framework. CoRe-UIE combines a content-preserving shared expert with four shared-backbone routed experts for color correction, scattering suppression, texture recovery, and illumination protection. The routed experts share the same architecture but have independent parameters, and are assigned to different regions through input-derived degradation cues and region-adaptive Top-\(k\) routing. We further introduce a Hilbert--Schmidt Independence Criterion (HSIC)-based representation constraint to reduce statistical dependence among expert features and alleviate redundant expert responses. Experiments on UIEB, LSUI, and U45 demonstrate that CoRe-UIE achieves competitive quantitative performance and visually balanced enhancement under diverse underwater degradation conditions.
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