CoRE-UIR

Official pretrained models for CoRE-UIR: Prior-Guided Common and Residual Experts for Efficient All-in-One Remote Sensing Image Restoration.

[Paper] [Code] [MDVD-108K] [Project Page]

Model Description

CoRE-UIR is a prior-guided global-local framework for restoring diverse single and compound degradations in remote sensing imagery. Its primary structural innovation, the Common-and-Residual Expert (CoRE) block, combines a common dense expert for degradation-invariant restoration with sparsely routed low-rank residual experts for degradation-specific compensation.

Available Checkpoints

Benchmark	DPE adapter	Restoration model	Routing
MDVD-108K	dpe_clip_b32_mdvd.ckpt	core_uir_mdvd.pth	6 experts, Top-3
MDRS-Landsat	dpe_clip_b32_mdrs.ckpt	core_uir_mdrs.pth	4 experts, Top-1

The DPE files contain the trained adapter and classification head. The frozen openai/clip-vit-base-patch32 backbone is loaded separately from Hugging Face.

Usage

Install the official implementation:

git clone https://github.com/zzaiyan/CoRE-UIR.git
cd CoRE-UIR
pip install -r requirements.txt

Evaluate on MDVD-108K:

python test.py \
  -opt configs/core_uir_mdvd.yml \
  --weights hf://zzaiyan/CoRE-UIR/core_uir_mdvd.pth \
  --gpu 0 \
  --no-resume

Evaluate on MDRS-Landsat:

python test.py \
  -opt configs/core_uir_mdrs.yml \
  --weights hf://zzaiyan/CoRE-UIR/core_uir_mdrs.pth \
  --gpu 0 \
  --no-resume

The public configurations download the matching DPE adapter automatically and reuse files from the local Hugging Face cache.

Citation

@article{zhang2026coreuir,
  title     = {{CoRE-UIR: Prior-guided common and residual experts for efficient all-in-one remote sensing image restoration}},
  author    = {Zhang, Zaiyan and Yuan, Qiangqiang and Li, Jie and Lihe, Ziyang and Wan, Yu and Chen, Yuzeng and Su, Xin and Zhang, Liangpei},
  journal   = {ISPRS Journal of Photogrammetry and Remote Sensing},
  volume    = {240},
  pages     = {410--429},
  year      = {2026},
  publisher = {Elsevier},
  doi       = {10.1016/j.isprsjprs.2026.07.028}
}