NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge
Jie Liang, Radu Timofte, Qiaosi Yi, Shuaizheng Liu, Lingchen Sun, Rongyuan Wu, Xindong Zhang, Hui Zeng, Lei Zhang, Yibin Huang, Shuai Liu, Yongqiang Li, Chaoyu Feng, Xiaotao Wang, Lei Lei, Yuxiang Chen, Xiangyu Chen, Qiubo Chen, Fengyu Sun, Mengying Cui, Jiaxu Chen, Zhenyu Hu, Jingyun Liu, Wenzhuo Ma, Ce Wang, Hanyou Zheng, Wanjie Sun, Zhenzhong Chen, Ziwei Luo, Fredrik Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön, Xiong Dun, Pengzhou Ji, Yujie Xing, Xuquan Wang, Zhanshan Wang, Xinbin Cheng, Jun Xiao, Chenhang He, Xiuyuan Wang, Zhi-Song Liu, Zimeng Miao, Zhicun Yin, Ming Liu, Wangmeng Zuo, Shuai Li
Abstract
In this paper, we review the NTIRE 2024 challenge on Restore Any Image Model (RAIM) in the Wild. The RAIM challenge constructed a benchmark for image restoration in the wild, including real-world images with/without reference ground truth in various scenarios from real applications. The participants were required to restore the real-captured images from complex and unknown degradation, where generative perceptual quality and fidelity are desired in the restoration result. The challenge consisted of two tasks. Task one employed real referenced data pairs, where quantitative evaluation is available. Task two used unpaired images, and a comprehensive user study was conducted. The challenge attracted more than 200 registrations, where 39 of them submitted results with more than 400 submissions. Top-ranked methods improved the state-of-the-art restoration performance and obtained unanimous recognition from all 18 judges. The proposed datasets are available at https : //drive.google.com/file/d/1DqbxUoiUqkAIkExu3jZAqoElr_nu1IXb/view?usp=sharing and the homepage of this challenge is at https : //codalab.lisn.upsaclay.fr/competitions/17632.