AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results
George Ciubotariu, Florin-Alexandru Vasluianu, Zhuyun Zhou, Nancy Mehta, Radu Timofte, Ke Wu, Long Sun, Lingshun Kong, Zhongbao Yang, Jinshan Pan, Jiangxin Dong, Jinhui Tang, Hao Chen, Yinghui Fang, Dafeng Zhang, Yongqi Song, Jiangbo Guo, Shuhua Jin, Zeyu Xiao, Rui Zhao, Zhuoyuan Li, Cong Zhang, Yufeng Peng, Xin Lu, Zhijing Sun, Chengjie Ge, Zihao Li, Zishun Liao, Ziang Zhou, Qiyu Kang, Xueyang Fu, Zheng-Jun Zha, Yuqian Zhang, Shuai Liu, Jie Liu, Zhuhao Zhang, Lishen Qu, Zhihao Liu, Shihao Zhou, Yaqi Luo, Juncheng Zhou, Jufeng Yang, Qianfeng Yang, Qiyuan Guan, Xiang Chen, Guiyue Jin, Jiyu Jin
公開日: 2025/9/8
Abstract
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.