Event-based Motion Deblurring with Unpaired Data

We introduce EMP, an event-based motion deblurring framework that operates entirely in an unpaired setting, removing the need for aligned blur–sharp supervision. EMP bridges the disjoint blur and sharp domains through event information and leverages two complementary training mechanisms: (1) an event-based physical prior with confidence masking that provides reliable self-supervisory signals for blurry inputs, and (2) a generative blur modeling process that extracts blur-related frequency-domain cues from blur–event pairs and transfers them to sharp images to synthesize realistic blur. Extensive experiments on various real-event datasets, including REBlur, EventAid, and HighREV, show that EMP outperforms existing unpaired baselines and achieves performance competitive with paired methods.