Asymmetric Hierarchical Difference-aware Interaction Network for Event-guided Motion Deblurring

Event cameras are bio-inspired sensors that are capable of capturing motion information with high temporal resolution, which show potential in aiding image motion deblurring recently. Most existing methods indiscriminately handle feature fusion of two modalities with symmetric unidirectional/bidirectional interactions at different-level layers in feature encoder, while ignoring the different dependencies between cross-modal hierarchical features. To tackle these limitations, we propose a novel Asymmetric Hierarchical Difference-aware Interaction Network (AHDINet) for event-based motion deblurring, which explores the complementarity of two modalities with differential dependence modeling of cross-modal hierarchical features. Thereby, an event-assisted edge complement module is designed to leverage event modality to enhance the edge details of the image features in low-level encoder stage, and an image-assisted semantic complement module is developed to transfer contextual semantics of image features to event branch in high-level encoder stage. Benefiting from the proposed differentiated interaction mode, the respective advantages of image and event modalities are fully exploited. Extensive experiments on both synthetic and real-world datasets demonstrate that our method achieves state-of-the-art performance.