发表机构
School of Geodesy and Geomatics, Wuhan University; School of Artificial Intelligence, Wuhan University; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘学院; 武汉大学人工智能学院; 武汉大学测绘遥感信息工程国家重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CoRE-UIR是一种先验引导的一体化遥感图像复原模型，通过通用与残差专家块实现高效复原，在PSNR提升1.05dB的同时速度提升11.83倍、内存降低85.3%，并构建了相关数据集验证泛化性。
AI 中文摘要
无人机（UAV）和卫星获取的遥感图像常受恶劣天气、光照变化及成像伪影退化，这些退化因素可共同出现，引发全局分布偏移与局部结构损坏。尽管一体化图像复原为特定任务的处理流程提供了极具吸引力的统一替代方案，但现有方法仍存在退化线索薄弱或不明确的问题，且因采用具有重叠复原行为的满秩多专家设计而导致参数冗余。我们提出CoRE-UIR（通用图像复原的通用与残差专家模型），这是一种以通用与残差专家块（CoRE）为核心的先验引导式全局-局部框架。CoRE明确将复原能力分解为用于退化不变复原的通用密集专家，以及用于退化特定补偿的低秩残差专家，从而实现自适应专业化，避免冗余专家复制。基于此设计，退化先验嵌入（DPE）将冻结的CLIP特征适配为明确的复原导向先验，而全局特征调制（GFM）则在局部残差补偿前对齐全局特征统计。我们还构建了MDVD-108K（多退化VisDrone），这是一个涵盖单一及复合退化的大规模无人机复原数据集，同时构建了真实世界测试集。在多个数据集上的大量实验表明，与最强基线BaryIR相比，CoRE-UIR将整体平均峰值信噪比（PSNR）提升了1.05 dB，运行速度提高了11.83倍，峰值内存降低了85.3%，从而维持了良好的质量-效率平衡。下游任务和未见退化的评估也验证了CoRE-UIR的泛化性。代码和数据集将发布在该https URL。
英文摘要
Remote sensing images acquired by unmanned aerial vehicles (UAVs) and satellites are often degraded by adverse weather, illumination variation, and imaging artifacts, which may co-occur and jointly induce global distribution shifts and local structural corruption. Although All-in-One image restoration offers an appealing unified alternative to task-specific pipelines, existing methods still suffer from weak or implicit degradation cues and parameter redundancy caused by full-rank multi-expert designs with overlapping restoration behaviors. We propose CoRE-UIR (Common and Residual Experts for Universal Image Restoration), a prior-guided global-local framework centered on the Common-and-Residual Expert Block (CoRE). CoRE explicitly decomposes restoration capacity into a common dense expert for degradation-invariant restoration and low-rank residual experts for degradation-specific compensation, enabling adaptive specialization without redundant expert replication. Built on this design, Degradation Prior Embedding (DPE) adapts frozen CLIP features into an explicit restoration-oriented prior, while Global Feature Modulation (GFM) aligns global feature statistics before local residual compensation. We also construct MDVD-108K (Multi-Degradation VisDrone), a large-scale UAV restoration dataset covering both single and compound degradations, together with a real-world test set. Extensive experiments on multiple datasets show that CoRE-UIR improves the overall average PSNR by 1.05 dB while running 11.83$\times$ faster and reducing peak memory by 85.3% relative to the strongest baseline, BaryIR, thereby maintaining a favorable quality-efficiency trade-off. Evaluations on downstream tasks and unseen degradation also validate the generalizability of CoRE-UIR. The code and dataset will be released at https://github.com/zzaiyan/CoRE-UIR.
CommentsAccepted by ISPRS Journal of Photogrammetry and Remote Sensing
