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[bibtex]@InProceedings{Ciubotariu_2025_ICCV, author = {Ciubotariu, George and Vasluianu, Florin-Alexandru and Zhou, Zhuyun and Mehta, Nancy and Timofte, Radu and Wu, Ke and Sun, Long and Kong, Lingshun and Yang, Zhongbao and Pan, Jinshan and Dong, Jiangxin and Tang, Jinhui and Chen, Hao and Fang, Yinghui and Zhang, Dafeng and Song, Yongqi and Guo, Jiangbo and Jin, Shuhua and Xiao, Zeyu and Zhao, Rui and Li, Zhuoyuan and Zhang, Cong and Peng, Yufeng and Lu, Xin and Sun, Zhijing and Ge, Chengjie and Li, Zihao and Liao, Zishun and Zhou, Ziang and Kang, Qiyu and Fu, Xueyang and Zha, Zheng-Jun and Zhang, Yuqian and Liu, Shuai and Liu, Jie and Zhang, Zhuhao and Qu, Lishen and Liu, Zhihao and Zhou, Shihao and Luo, Yaqi and Zhou, Juncheng and Yang, Jufeng and Yang, Qianfeng and Guan, Qiyuan and Chen, Xiang and Jin, Guiyue and Jin, Jiyu}, title = {AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2025}, pages = {5600-5609} }
AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results
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.
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