UniDemoir\é: Towards Universal Image Demoir\éing with Data Generation and Synthesis

Image demoiréing poses one of the most formidable challenges in image restoration, primarily due to the unpredictable and anisotropic nature of moiré patterns. Limited by the quantity and diversity of training data, current methods tend to overfit to a single moiré domain, resulting in performance degradation for new domains and restricting their robustness in real-world applications. In this paper, we propose a universal image demoiréing solution, UniDemoiré, which has superior generalization capability. Notably, we propose innovative and effective data generation and synthesis methods that can automatically provide vast high-quality moiré images to train a universal demoiréing model. Our extensive experiments demonstrate the cutting-edge performance and broad potential of our approach for generalized image demoiréing.

@article{yang2025_2502.06324,
  title={ UniDemoir\é: Towards Universal Image Demoir\éing with Data Generation and Synthesis },
  author={ Zemin Yang and Yujing Sun and Xidong Peng and Siu Ming Yiu and Yuexin Ma },
  journal={arXiv preprint arXiv:2502.06324},
  year={ 2025 }
}