  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.
