RealRestorer/RealRestorer_degradation_models

RealRestorer Degradation Models

This repository provides the degradation models used in the degradation pipeline of RealRestorer.

In particular, these models are used for moire synthesis and specular reflection synthesis in the RealRestorer degradation pipeline, which are designed to generate realistic degradations for training data construction and controlled degradation simulation.

Usage

Please refer to the main RealRestorer repository for the complete pipeline, code, and usage instructions:

• RealRestorer: https://github.com/yfyang007/RealRestorer

These degradation models are intended to be used together with the degradation pipeline in RealRestorer rather than as a standalone image restoration model.

Related Links

• Project Page: https://yfyang007.github.io/RealRestorer/
• RealRestorer Model: https://huggingface.co/RealRestorer/RealRestorer
• RealIR-Bench: https://huggingface.co/datasets/RealRestorer/RealIR-Bench

Citation

If you find RealRestorer useful in your research, please star and cite:

@misc{yang2026realrestorergeneralizablerealworldimage,
      title={RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models},
      author={Yufeng Yang and Xianfang Zeng and Zhangqi Jiang and Fukun Yin and Jianzhuang Liu and Wei Cheng and jinghong lan and Shiyu Liu and Yuqi Peng and Gang YU and Shifeng Chen},
      year={2026},
      eprint={2603.25502},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.25502},
}

@misc{yang2025unidemoire,
  author    = {Zemin Yang, Yujing Sun, Xidong Peng, Siu Ming Yiu, Yuexin Ma},
  title     = {UniDemoiré: Towards Universal Image Demoiréing with Data Generation and Synthesis},
  year      = {2025},
  eprint    = {2502.06324},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url={https://arxiv.org/abs/2502.06324},
}

@inproceedings{wen2019single,
  title={Single image reflection removal beyond linearity},
  author={Wen, Qiang and Tan, Yinjie and Qin, Jing and Liu, Wenxi and Han, Guoqiang and He, Shengfeng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3771--3779},
  year={2019}
}