Learning Dual Modality Interactions for Event-Based Motion Deblurring

Event cameras hold great potential for motion deblurring because they capture motion information with microsecond precision, offering robustness to motion blur. However, the limited interaction between RGB frames and event streams presents a significant challenge, preventing the full utilization of the event cameras' unique advantages. To address this, we proposeDual frame-eventInteraction and introduce a multi-scaleNetwork structure, DuInt-Net. DuInt-Net aims to tackle two key challenges: (1) enhancing the representational and interaction capabilities between RGB frames and event streams, and (2) adaptively selecting richer visual features for improved motion deblurring. We introduce an event-frame joint interaction module that consists of three branches: a base branch, a global awareness attention branch, and a local enhancement attention branch. The base branch processes essential pixel-level features that retain the original structural information. The global branch integrates event data to improve large-scale motion understanding, while the local branch uses large-kernel convolutions to refine fine-grained details in RGB frames. For superior reconstruction performance, we also propose the event-guided multi-scale fusion attention module, which effectively combines local visual information and global frame-event relationships. Extensive experiments demonstrate that DuInt-Net achieves superior performance, both quantitatively and qualitatively, showcasing its superior motion deblurring capabilities.