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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.06793 |
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| _version_ | 1866915484324069376 |
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| author | Ciubotariu, George Vasluianu, Florin-Alexandru Zhou, Zhuyun Mehta, Nancy Timofte, Radu Wu, Ke Sun, Long Kong, Lingshun Yang, Zhongbao Pan, Jinshan Dong, Jiangxin Tang, Jinhui Chen, Hao Fang, Yinghui Zhang, Dafeng Song, Yongqi Guo, Jiangbo Jin, Shuhua Xiao, Zeyu Zhao, Rui Li, Zhuoyuan Zhang, Cong Peng, Yufeng Lu, Xin Sun, Zhijing Ge, Chengjie Li, Zihao Liao, Zishun Zhou, Ziang Kang, Qiyu Fu, Xueyang Zha, Zheng-Jun Zhang, Yuqian Liu, Shuai Liu, Jie Zhang, Zhuhao Qu, Lishen Liu, Zhihao Zhou, Shihao Luo, Yaqi Zhou, Juncheng Yang, Jufeng Yang, Qianfeng Guan, Qiyuan Chen, Xiang Jin, Guiyue Jin, Jiyu |
| author_facet | Ciubotariu, George Vasluianu, Florin-Alexandru Zhou, Zhuyun Mehta, Nancy Timofte, Radu Wu, Ke Sun, Long Kong, Lingshun Yang, Zhongbao Pan, Jinshan Dong, Jiangxin Tang, Jinhui Chen, Hao Fang, Yinghui Zhang, Dafeng Song, Yongqi Guo, Jiangbo Jin, Shuhua Xiao, Zeyu Zhao, Rui Li, Zhuoyuan Zhang, Cong Peng, Yufeng Lu, Xin Sun, Zhijing Ge, Chengjie Li, Zihao Liao, Zishun Zhou, Ziang Kang, Qiyu Fu, Xueyang Zha, Zheng-Jun Zhang, Yuqian Liu, Shuai Liu, Jie Zhang, Zhuhao Qu, Lishen Liu, Zhihao Zhou, Shihao Luo, Yaqi Zhou, Juncheng Yang, Jufeng Yang, Qianfeng Guan, Qiyuan Chen, Xiang Jin, Guiyue Jin, Jiyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06793 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results Ciubotariu, George Vasluianu, Florin-Alexandru Zhou, Zhuyun Mehta, Nancy Timofte, Radu Wu, Ke Sun, Long Kong, Lingshun Yang, Zhongbao Pan, Jinshan Dong, Jiangxin Tang, Jinhui Chen, Hao Fang, Yinghui Zhang, Dafeng Song, Yongqi Guo, Jiangbo Jin, Shuhua Xiao, Zeyu Zhao, Rui Li, Zhuoyuan Zhang, Cong Peng, Yufeng Lu, Xin Sun, Zhijing Ge, Chengjie Li, Zihao Liao, Zishun Zhou, Ziang Kang, Qiyu Fu, Xueyang Zha, Zheng-Jun Zhang, Yuqian Liu, Shuai Liu, Jie Zhang, Zhuhao Qu, Lishen Liu, Zhihao Zhou, Shihao Luo, Yaqi Zhou, Juncheng Yang, Jufeng Yang, Qianfeng Guan, Qiyuan Chen, Xiang Jin, Guiyue Jin, Jiyu Computer Vision and Pattern Recognition 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. |
| title | AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.06793 |