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.

该研究回顾了NTIRE 2024挑战赛中的图像恢复模型(RAIM)在野外的应用。该挑战建立了一个野外图像恢复的基准，包括真实应用中的各种场景中有/无参考真实图像。参与者需要恢复复杂和未知的退化的真实拍摄图像，期望在恢复结果中具有生成感知质量和保真度。挑战包括了两个任务。第一个任务使用真实参考数据对，可以进行定量评估。第二个任务使用非配对图像，并进行了全面的用户研究。该挑战吸引了200多个注册者，其中39人提交了超过400份结果。排名靠前的方法改进了最先进的恢复性能，并获得了18位评委的一致认可。所提出的数据集可在此https URL找到，该挑战的主页可在此https URL找到。

NTIRE 2024 野外图像恢复模型竞赛