Image demoir\'eing poses one of the most formidable challenges in image restoration, primarily due to the unpredictable and anisotropic nature of moir\'e patterns. Limited by the quantity and diversity of training data, current methods tend to overfit to a single moir\'e domain, resulting in performance degradation for new domains and restricting their robustness in real-world applications. In this paper, we propose a universal image demoir\'eing solution, UniDemoir\'e, which has superior generalization capability. Notably, we propose innovative and effective data generation and synthesis methods that can automatically provide vast high-quality moir\'e images to train a universal demoir\'eing model. Our extensive experiments demonstrate the cutting-edge performance and broad potential of our approach for generalized image demoir\'eing.
@article{arxiv.2502.06324,
title = {UniDemoir\'e: Towards Universal Image Demoir\'eing 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}
}