ALNet: An Adaptive Lateral-Interaction Spiking Neural Network for Event-Based Motion Deblurring

Abstract

Motion deblurring is an essential capability for high-speed vision applications such as autonomous driving and unmanned aerial vehicles. Existing frame-based deblurring methods often struggle with rapid motion, whereas event cameras offer a promising alternative. However, irregular motion patterns and event noise remain challenging. To address these issues, we propose ALNet, an adaptive lateral-interaction spiking neural network for event-driven motion deblurring. ALNet adopts a dual-branch image–event architecture. In the event branch, we develop a Lateral Deformable Interaction Spiking Neural Network (LDSNN), which introduces lateral spike interactions and adapts the offset-and-modulation mechanisms of deformable convolution to the lateral interactions of spiking neurons. The design dynamically adjusts the interaction region, aiming to enhance motion boundary modeling and reduce sensitivity to event perturbations. For temporal feature extraction from event streams, we design an Image-Guided Spiking Transformer (IGST), which uses image-domain spatial context to guide the temporal processing of event spikes. For image–event feature fusion, we introduce a Cross-Modal Attention Fusion module (CMAF) to align and selectively fuse multi-modal features, thereby improving edge reconstruction. Experiments on the GoPro, REBlur, and Ev-REDS datasets show that ALNet contains only 6.08 M parameters while achieving competitive PSNR and SSIM performance, providing a favorable trade-off between restoration accuracy and model compactness.