Event-Based Fusion for Motion Deblurring with Cross-modal Attention

Understand

Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times.

• As a kind of bio-inspired camera, the event camera records the intensity changes in an asynchronous way with high temporal resolution, providing valid image degradation information within the exposure time.
• In this paper, we rethink the eventbased image deblurring problem and unfold it into an end-to-end two-stage image restoration network.
• To effectively fuse event and image features, we design an event-image cross-modal attention module applied at multiple levels of our network, which allows to focus on relevant features from the event branch and filter out noise.