Ultra-high-speed cameras frequently suffer from severe overexposure in scenarios involving extreme brightness transitions, significantly degrading image quality and obscuring critical visual details. To address this issue, we propose a novel reconstruction method combining neuromorphic sensors with state-of-the-art diffusion models. Our approach leverages the asynchronous, high-temporal-resolution, and high-dynamic-range capabilities of neuro-morphic sensors to capture rapid brightness variations, subsequently utilizing conditional diffusion models to reconstruct high-quality frames from sparse event data. We validated the proposed method through experiments conducted under three challenging lighting conditions. The results demonstrate that our approach effectively recovers detailed visual content in severely overexposed frames, significantly outperforming traditional frame-based imaging techniques.