Event deblurring via feature enhancement and lightweight attention

Single-frame image deblurring remains an inherently ill-posed problem. Furthermore, existing diffusion models suffer from high inference latency, while state space models lack sufficient cross-modal interaction capabilities. To overcome these limitations, we propose an end-to-end Event-fusion Multi-head Attention Network (EFMAN) that exploits high-frequency spatiotemporal priors from event cameras for high-quality image restoration. Specifically, a cross-modal adaptive attention mechanism is designed to precisely align asynchronous high-frequency event streams with synchronous RGB features in both spatial and temporal dimensions, thereby compensating for exposure deficiencies. To mitigate the impact of inherent sensor noise, a Feature Enhancement Attention (FEA) module bolsters feature robustness against noise via global context modeling. Additionally, a Lightweight Channel-Spatial Attention (LCSA) module is integrated to adaptively recalibrate feature responses while substantially alleviating computational redundancy. These components are optimized by a multidimensional joint loss function—encompassing pixel, feature, and gradient domains—to synergistically enforce multi-scale constraints, ensuring consistency between micro-textures and global topologies. Extensive experiments demonstrate that EFMAN significantly enhances deblurring performance while maintaining efficient inference. Compared to state-of-the-art methods, our approach achieves maximum PSNR and SSIM improvements of 1.19 dB and 0.005 on the GoPro dataset, and 0.38 dB and 0.003 on the REBlur dataset, respectively. By effectively addressing the challenges of multi-modal alignment and noise interference, EFMAN strikes an optimal balance between restoration quality and computational efficiency, making it highly suitable for clear image reconstruction in high-dynamic-range and rapid-motion scenarios.