AI 中文总结
针对水下图像多种区域共存退化问题，提出CoRe-UIE专家协作框架，结合共享专家与路由专家，经实验在多数据集上取得均衡增强效果。
AI 中文摘要
水下图像常存在多种共存的退化问题，包括颜色失真、散射雾状、纹理衰减和光照不均。这些退化在不同区域存在差异且可能局部共存，使得传统的均匀恢复方法难以适应不同退化模式。为解决该问题，本文提出面向水下图像增强的共存与区域退化框架CoRe-UIE，这是一种面向退化的专家协作框架。CoRe-UIE结合了内容保留的共享专家与四个共享骨干的路由专家，分别用于颜色校正、散射抑制、纹理恢复和光照保护。这些路由专家具有相同架构但参数独立，通过输入衍生的退化线索和区域自适应Top-k路由被分配到不同区域。我们进一步引入基于希尔伯特-施密特独立性准则（HSIC）的表示约束，以减少专家特征间的统计依赖并缓解冗余专家响应。在UIEB、LSUI和U45数据集上的实验表明，CoRe-UIE在多种水下退化条件下实现了有竞争力的定量性能和视觉均衡的增强效果。
英文摘要
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.
Comments9 pages, 5 figures
