AI 中文总结
本研究提出CoDe-SSM模型，通过GCSM与LHFM双通路解耦处理UHD图像的上下文与细节，在5个基准5类退化任务上，实现了复原质量与效率的显著提升。
AI 中文摘要
超高清（UHD）图像复原需平衡空间重复退化线索的聚合与局部图像结构的保留，紧凑聚合可减少冗余处理但可能弱化边缘、纹理等精细结构。现有方法通过下采样、窗口划分或基于聚类的token缩减控制UHD复原成本，但多数未显式保留共享聚合难以表征的信息。本研究提出用于UHD复原的上下文-细节解耦状态空间模型（CoDe-SSM），其通过独立通路处理聚合上下文与聚类残差：上下文建模通路由全局聚类扫描模块（GCSM）实现，将特征聚合为K个依赖输入的聚类中心，并对所得固定阶序列应用选择性SSM推理，实现跨区域上下文共享，同时使计算成本与空间分辨率解耦；细节恢复通路由局部高频模块（LHFM）实现，通过输入衍生的高频掩码与稀疏卷积专家混合体处理聚类残差。在5个UHD基准和5种退化类型上的大量实验表明，本研究的显式上下文-细节解耦策略在保持理想效率的同时，复原质量获得显著提升。
英文摘要
Ultra-high-definition (UHD) image restoration must balance the aggregation of spatially recurring degradation cues with the preservation of localized image structures. Compact aggregation can reduce redundant processing but may attenuate edges, textures, and other fine structures. Existing approaches manage UHD restoration cost through downsampling, window partitioning, or cluster-based token reduction; yet many of them do not explicitly retain information that is poorly represented by shared aggregation. In this study, we propose a Context-Detail Decoupled State Space Model (CoDe-SSM) for UHD restoration, which processes aggregated context and clustering residuals in separate pathways. The context modeling pathway, implemented by the Global Cluster Scan Module (GCSM), aggregates features into $K$ input-dependent cluster centers and applies selective SSM reasoning over the resulting fixed-order sequence, enabling cross-region context sharing while decoupling computational cost from spatial resolution. The detail recovery pathway, implemented by the Local High-Frequency Module (LHFM), processes the clustering residual with an input-derived high-frequency mask and a sparse mixture of convolutional experts. Extensive experiments on five UHD benchmarks and five degradation types demonstrate that our explicit context-detail decoupling strategy yields substantial gains in restoration quality while maintaining desirable efficiency.
