基于事件的无配对数据运动去模糊
Event-based Motion Deblurring with Unpaired Data
提出无配对事件去模糊框架，利用事件物理先验和生成式模糊建模，为缺乏配对数据的底层视觉任务提供可复用思路。
原文依据 we introduce EMP, an event-based motion deblurring framework that operates entirely in an unpaired setting
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论事件相机提供高时间分辨率、以运动为中心的测量，在快速运动和挑战性光照下依然可靠，使其成为运动去模糊的一种有前景的传感模态。然而，现有的去模糊方法通常需要大规模配对模糊-清晰数据集，这在真实场景中极难获取，尤其是当涉及事件等额外模态时。在本工作中，我们提出 EMP，一种完全在无配对设置下运行的事件运动去模糊框架，无需对齐的模糊-清晰监督。EMP 通过事件信息桥接不相交的模糊域和清晰域，并利用两种针对无配对情形量身定制的互补训练机制：(1) 基于事件的物理先验与置信度掩码，为模糊输入提供可靠的自监督信号；(2) 生成式模糊建模过程，从模糊-事件对中提取与模糊相关的频域线索，并将其迁移到清晰图像以合成逼真的模糊。这些机制共同使得无需配对标签即可实现稳定有效的去模糊。在包括 REBlur、EventAid 和 HighREV 在内的多种真实事件数据集上的大量实验表明，EMP 优于现有的无配对基线，并达到与配对方法相竞争的性能。
英文原文摘要 1372 字符
Event cameras provide high-temporal-resolution, motion-centric measurements that remain reliable under fast motion and challenging illumination, making them a promising sensing modality for motion deblurring. However, existing deblurring methods typically require large-scale paired blur--sharp datasets, which are extremely difficult to obtain in real-world settings, especially when an additional modality such as events is involved. In this work, 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 tailored to the unpaired regime: (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. Together, these mechanisms enable stable and effective deblurring without paired labels. 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.