{"type":"ScholarlyArticle","title":"A Coarse-to-Fine Fusion Network for Event-Based Image Deblurring","authors":[{"name":"Huan Li","slug":"huan-li","given":"Huan","family":"Li","affiliation":"Institute of Microelectronics , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Hailong Shi","slug":"hailong-shi","given":"Hailong","family":"Shi","affiliation":"Institute of Microelectronics , Chinese Academy of Sciences , Beijing , China"},{"name":"Xingyu Gao","slug":"xingyu-gao","given":"Xingyu","family":"Gao","affiliation":"Institute of Microelectronics , Chinese Academy of Sciences , Beijing , China"}],"abstract":"Event-driven image deblurring is an innovative approach involving the input of events obtained from the event camera alongside blurred frames to facilitate the deblurring process. Unlike conventional cameras, event cameras in event-driven imaging exhibit low-latency characteristics and are immune to motion blur, resulting in significant advancements in image deblurring. In this paper, we propose a pioneering event-based coarse-to-fine image deblurring network named CFFNet. In contrast to existing deblurring methods, our approach incorporates event data, generating multiple coarse frames from a single frame before further refining them into a sharp image. We introduce an Event Image Fusion Block (EIFB) for the coarse fusion of events and images, producing coarse frames at different time points. Additionally, we propose a Bidirectional Frame Fusion Block (BFFB) for the fine fusion of coarse frames. CFFNet effectively harnesses the spatiotemporal information of event data through a comprehensive fusion process from coarse to fine. Experimental results on the GoPro and REBlur datasets demonstrate that our method achieves state-of-the-art performance for image deblurring task.","doi":"10.24963/ijcai.2024/108","url":"https://ijcai.org/proceedings/2024/108","pdf":"https://ijcai.org/proceedings/2024/0108.pdf","markdown":"https://ijcai.org/proceedings/2024/108.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/108","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":108,"pages":{"first":974,"last":982},"keywords":["Computer Vision: CV: Applications"],"videos":[],"references":[{"position":1,"raw":"[Cao et al., 2022] Chengzhi Cao, Xueyang Fu, Yurui Zhu, Gege Shi, and Zheng-Jun Zha. 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