TY - GEN
T1 - NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge
AU - Liang, Jie
AU - Timofte, Radu
AU - Yi, Qiaosi
AU - Zhang, Zhengqiang
AU - Liu, Shuaizheng
AU - Sun, Lingchen
AU - Wu, Rongyuan
AU - Zhang, Xindong
AU - Zeng, Hui
AU - Zhang, Lei
AU - Hao, Tianyu
AU - Wang, Lin
AU - Xiao, Zhe
AU - Ji, Pengzhou
AU - Zhong, Shu Peng
AU - Wang, Xiangming
AU - Yan, Jiaqi
AU - Pan, Sishun
AU - Wang, Ce
AU - Huang, Yibin
AU - Wang, Zhan Sheng
AU - Liang, Haobo
AU - Pan, Zhenghao
AU - Wu, Jinjian
AU - Zuo, Yushen
AU - Zhou, Yuanbo
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/9
Y1 - 2025/9
N2 - 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 at https://drive.google.com/drive/folders/1Mgqve-yNcE26IIieI8lMIf-25VvZRs_J and https://drive.google.com/drive/folders/1UB7nnzLwqDZOwDmD9aT8J0KVg2ag4Qae, respectively. The official challenge pages for Track 1 and Track 2 can be found at https://codalab.lisn.upsaclay.fr/competitions/21334#learn_the_details and https://codalab.lisn.upsaclay.fr/competitions/21623#learn_the_details.
AB - 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 at https://drive.google.com/drive/folders/1Mgqve-yNcE26IIieI8lMIf-25VvZRs_J and https://drive.google.com/drive/folders/1UB7nnzLwqDZOwDmD9aT8J0KVg2ag4Qae, respectively. The official challenge pages for Track 1 and Track 2 can be found at https://codalab.lisn.upsaclay.fr/competitions/21334#learn_the_details and https://codalab.lisn.upsaclay.fr/competitions/21623#learn_the_details.
UR - https://www.scopus.com/pages/publications/105017855326
U2 - 10.1109/CVPRW67362.2025.00119
DO - 10.1109/CVPRW67362.2025.00119
M3 - Conference article published in proceeding or book
AN - SCOPUS:105017855326
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 1260
EP - 1269
BT - Proceedings - 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2025
PB - IEEE Computer Society
T2 - 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2025
Y2 - 11 June 2025 through 12 June 2025
ER -