Event-Enhanced Snapshot Mosaic Hyperspectral Frame Deblurring

Abstract

Snapshot Mosaic Hyperspectral Cameras (SMHCs) are popular hyperspectral imaging devices for acquiring both color and motion details of scenes. However, the narrow-band spectral filters in SMHCs may negatively impact their motion perception ability, resulting in blurry SMHC frames. In this paper, we propose a hardware-software collaborative approach to address the blurring issue of SMHCs. Our approach involves integrating SMHCs with neuromorphic event cameras for efficient event-enhanced SMHC frame deblurring. To achieve spectral information recovery guided by event signals, we formulate a spectral-aware Event-based Double Integral (sEDI) model that links SMHC frames and events from a spectral perspective, providing principled model design insights. Then, we develop a Diffusion-guided Noise Awareness (DNA) training framework that utilizes diffusion models to learn noise-aware features and promote model robustness towards camera noise. Furthermore, we design an Event-enhanced Hyperspectral frame Deblurring Network (EvHDNet) based on sEDI, which is trained with DNA and features improved spatial-spectral learning and modality interaction for reliable SMHC frame deblurring. Experiments on both synthetic data and real data show that the proposed DNA + EvHDNet outperforms state-of-the-art methods on both spatial and spectral fidelity. The code and dataset will be made publicly available.