Wenlong Jiao, Heyang Lee, Ping Wang, Pengfei Zhu, Qinghua Hu, Dongwei Ren

Abstract: All-in-one image restoration aims to handle diverse degradations (e.g., noise, blur, adverse weather) within a unified framework, yet existing methods increasingly rely on complex architectures (e.g., Mixture-of-Experts, diffusion models) and elaborate degradation prompt strategies. In this work, we reveal a critical insight: well-crafted feature extraction inherently encodes degradation-carrying information, and a symmetric U-Net architecture is sufficient to unleash these cues effectively. By aligning feature scales across encoder-decoder and enabling streamlined cross-scale propagation, our symmetric design preserves intrinsic degradation signals robustly, rendering simple additive fusion in skip connections sufficient for state-of-the-art performance. Our primary baseline, SymUNet, is built on this symmetric U-Net and achieves better results across benchmark datasets than existing approaches while reducing computational cost. We further propose a semantic enhanced variant, SE-SymUNet, which integrates direct semantic injection from frozen CLIP features via simple cross-attention to explicitly amplify degradation priors. Extensive experiments on several benchmarks validate the superiority of our methods. Both baselines SymUNet and SE-SymUNet establish simpler and stronger foundations for future advancements in all-in-one image restoration.

conda create -n symunet python=3.12
conda activate symunet
pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0
pip install -r requirements.txt
pip install --no-build-isolation -e .

CUDA_VISIBLE_DEVICES=x,x torchrun --nproc_per_node=x --master_port=xxxxx basicsr/train.py -opt options/train/AllInOne/xxx.yml --launcher pytorch

CUDA_VISIBLE_DEVICES=x,x torchrun --nproc_per_node=x --master_port=xxxxx basicsr/test.py -opt options/test/AllInOne/xxx.yml --launcher pytorch

@article{jiao2025unleashing,
  title={Unleashing Degradation-Carrying Features in Symmetric U-Net: Simpler and Stronger Baselines for All-in-One Image Restoration},
  author={Jiao, Wenlong and Lee, Heyang and Wang, Ping and Zhu, Pengfei and Hu, Qinghua and Ren, Dongwei},
  journal={arXiv preprint arXiv:2512.10581},
  year={2025}
}

This code is built upon NAFNet and BasicSR. We thank the authors for their excellent work.