-
[pdf]
[supp]
[arXiv]
[bibtex]@InProceedings{Liang_2025_CVPR, author = {Liang, Jie and Timofte, Radu and Yi, Qiaosi and Zhang, Zhengqiang and Liu, Shuaizheng and Sun, Lingchen and Wu, Rongyuan and Zhang, Xindong and Zeng, Hui and Zhang, Lei and Hao, Tianyu and Wang, Lin and Xiao, Zhe and Ji, Pengzhou and Zhong, Shu-Peng and Wang, Xiangming and Yan, Jiaqi and Pan, Sishun and Wang, Ce and Huang, Yibin and Wang, Zhang Sheng and Liang, Haobo and Pan, Zhenghao and Wu, Jinjian and Zuo, Yushen and Zhou, Yuanbo}, title = {NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops}, month = {June}, year = {2025}, pages = {1269-1278} }
NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge
Abstract
In this paper, we present a comprehensive overview of the NTIRE 2025 challenge on the 2nd Restore Any Image Model (RAIM) in the Wild. This challenge established a new benchmark for real-world image restoration, featuring diverse scenarios with and without reference ground truth. Participants were tasked with restoring real-captured images suffering from complex and unknown degradations, where both perceptual quality and fidelity were critically evaluated. The challenge comprised two tracks: (1) the low-light joint denoising and demosaicing (JDD) task, and (2) the image detail enhancement/generation task. Each track included two sub-tasks. The first sub-task involved paired data with available ground truth, enabling quantitative evaluation. The second sub-task dealt with real-world yet unpaired images, emphasizing restoration efficiency and subjective quality assessed through a comprehensive user study. In total, the challenge attracted nearly 300 registrations, with 51 teams submitting more than 600 results. The top-performing methods advanced the state of the art in image restoration and received unanimous recognition from all 20+ expert judges.
Related Material