This paper presents a comprehensive review of the AIM 2025 High FPS Non-Uniform Motion Deblurring Challenge, highlighting the proposed solutions and final results. The objective of this challenge is to identify effective networks capable of producing clearer and visually compelling images in diverse and challenging conditions, by learning representative visual cues for complex aggregations of motion types. A total of 68 participants registered for the competition, and 9 teams ultimately submitted valid entries. This paper thoroughly evaluates the state-of-the-art advances in high-FPS single image motion deblurring, showcasing the significant progress in the field, while leveraging samples of the novel dataset, MIORe, that introduces challenging examples of movement patterns.
@article{arxiv.2509.06793,
title = {AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results},
author = {George Ciubotariu and Florin-Alexandru Vasluianu and Zhuyun Zhou and Nancy Mehta and Radu Timofte and Ke Wu and Long Sun and Lingshun Kong and Zhongbao Yang and Jinshan Pan and Jiangxin Dong and Jinhui Tang and Hao Chen and Yinghui Fang and Dafeng Zhang and Yongqi Song and Jiangbo Guo and Shuhua Jin and Zeyu Xiao and Rui Zhao and Zhuoyuan Li and Cong Zhang and Yufeng Peng and Xin Lu and Zhijing Sun and Chengjie Ge and Zihao Li and Zishun Liao and Ziang Zhou and Qiyu Kang and Xueyang Fu and Zheng-Jun Zha and Yuqian Zhang and Shuai Liu and Jie Liu and Zhuhao Zhang and Lishen Qu and Zhihao Liu and Shihao Zhou and Yaqi Luo and Juncheng Zhou and Jufeng Yang and Qianfeng Yang and Qiyuan Guan and Xiang Chen and Guiyue Jin and Jiyu Jin},
journal= {arXiv preprint arXiv:2509.06793},
year = {2025}
}