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, Xiaoquian Lv, Lu Yang, Shuigen Wang, Shengping Zhang, Xiangyang Ji, Long Bao, Yuqiang Yang, Jinao Song, Ziyi Wang et al. (68 additional authors not shown)

Abstract: This paper presents an overview of the 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 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.