Papers › NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results
NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results
Lei Sun, Andrea Alfarano, Peiqi Duan, Shaolin Su, Kaiwei Wang, Boxin Shi, Radu Timofte, Danda Pani Paudel, Luc van Gool, Qinglin Liu, Wei Yu, Xiaoqian Lv, Lu Yang, Shuigen Wang, Shengping Zhang, Xiangyang Ji, Long Bao, Yuqiang Yang, Jinao Song, Ziyi Wang, Shuang Wen, Heng Sun, Kean Liu, Mingchen Zhong, Senyan Xu, Zhijing Sun, Jiaying Zhu, Chengjie Ge, Xingbo Wang, Yidi Liu, Xin Lu, Xueyang Fu, Zheng-Jun Zha, Dawei Fan, Dafeng Zhang, Yong Yang, Siru Zhang, Qinghua Yang, Hao Kang, Huiyuan Fu, Heng Zhang, Hongyuan Yu, Zhijuan Huang, Shuoyan Wei, Feng Li, Runmin Cong, Weiqi Luo, Mingyun Lin, Chenxu Jiang, Hongyi Liu, Lei Yu, WeiLun Li, Jiajun Zhai, Tingting Lin, Shuang Ma, Sai Zhou, Zhanwen Liu, Yang Wang, Eiffel Chong, Nuwan Bandara, Thivya Kandappu, Archan Misra, Yihang Chen, Zhan Li, Weijun Yuan, Wenzhuo Wang, Boyang Yao, Zhanglu Chen, Yijing Sun, Tianjiao Wan, Zijian Gao, Qisheng Xu, Kele Xu, Yukun Zhang, Yu He, Xiaoyan Xie, Tao Fu, Yashu Gautamkumar Patel, Vihar Ramesh Jain, Divesh Basina, Rishik Ashili, Manish Kumar Manjhi, Sourav Kumar, Prinon Benny, Himanshu Ghunawat, B Sri Sairam Gautam, Anett Varghese, Abhishek Yadav
This paper presents an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results. The primary goal of the challenge is to design an event-based method that achieves high-quality image deblurring, with performance quantitatively assessed using Peak Signal-to-Noise Ratio (PSNR). Notably, there are no restrictions on computational complexity or model size. The task focuses on leveraging both events and images as inputs for single-image deblurring. A total of 199 participants registered, among whom 15 teams successfully submitted valid results, offering valuable insights into the current state of event-based image deblurring. We anticipate that this challenge will drive further advancements in event-based vision research.
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