NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge
Jie Liang, Radu Timofte, Qiaosi Yi, Zhengqiang Zhang, Shuaizheng Liu, Lingchen Sun, Rongyuan Wu, Xindong Zhang, Hui Zeng, Lei Zhang

TL;DR
The NTIRE 2025 challenge introduced new benchmarks for real-world image restoration, focusing on diverse scenarios with complex degradations, and showcased state-of-the-art methods through extensive participant submissions and evaluations.
Contribution
This challenge provides a comprehensive benchmark for real-world image restoration tasks, including unpaired data scenarios, and advances the evaluation of restoration methods in practical settings.
Findings
Top methods achieved state-of-the-art restoration quality.
Nearly 300 registrations and 600 submissions demonstrate high community engagement.
Methods received unanimous expert recognition for performance.
Abstract
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…
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Taxonomy
TopicsMedical Imaging Techniques and Applications
