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This paper introduces CoRE-UIR, a novel framework for remote sensing image restoration that utilizes a Common-and-Residual Expert Block to separate degradation-invariant and degradation-specific restoration processes. By integrating a prior-guided approach with Degradation Prior Embedding and Global Feature Modulation, CoRE-UIR achieves significant improvements in both efficiency and restoration quality. Experimental results demonstrate that CoRE-UIR outperforms the leading baseline, BaryIR, with a 1.05 dB increase in average PSNR while being 11.83 times faster and reducing peak memory usage by 85.3%.
CoRE-UIR achieves a remarkable 1.05 dB PSNR improvement while being 11.83 times faster than the leading image restoration method, BaryIR.
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.
