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. The datasets used in Track 1 and Track 2 are available atthis https URLandthis https URL, respectively. The official challenge pages for Track 1 and Track 2 can be found atthis https URLandthis https URL.
View on arXiv@article{liang2025_2506.01394,
title={ NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge },
author={ Jie Liang and Radu Timofte and Qiaosi Yi and Zhengqiang Zhang and Shuaizheng Liu and Lingchen Sun and Rongyuan Wu and Xindong Zhang and Hui Zeng and Lei Zhang },
journal={arXiv preprint arXiv:2506.01394},
year={ 2025 }
}