By leveraging the blur-noise trade-off, imaging with non-uniform exposures largely extends the image acquisition flexibility in harsh environments. However, the limitation of conventional cameras in perceiving intra-frame dynamic information prevents existing methods from being implemented in the real-world frame acquisition for real-time adaptive camera shutter control. To address this challenge, we propose a novel Neuromorphic Shutter Control (NSC) system to avoid motion blurs and alleviate instant noises, where the extremely low latency of events is leveraged to monitor the real-time motion and facilitate the scene-adaptive exposure. Furthermore, to stabilize the inconsistent Signal-to-Noise Ratio (SNR) caused by the non-uniform exposure times, we propose an event-based image denoising network within a self-supervised learning paradigm, i.e., SEID, exploring the statistics of image noises and inter-frame motion information of events to obtain artificial supervision signals for high-quality imaging in real-world scenes. To illustrate the effectiveness of the proposed NSC, we implement it in hardware by building a hybrid-camera imaging prototype system, with which we collect a real-world dataset containing well-synchronized frames and events in diverse scenarios with different target scenes and motion patterns. Experiments on the synthetic and real-world datasets demonstrate the superiority of our method over state-of-the-art approaches.

翻译：通过利用模糊-噪声权衡，非均匀曝光成像极大扩展了恶劣环境下的图像采集灵活性。然而，传统相机在感知帧内动态信息方面的局限性，导致现有方法无法应用于支持实时自适应相机快门控制的真实帧采集场景。为应对这一挑战，我们提出一种新型神经形态快门控制（NSC）系统，通过利用事件极低延迟特性监测实时运动并实现场景自适应曝光，从而避免运动模糊并缓解瞬时噪声。此外，为稳定非均匀曝光时间引起的信噪比（SNR）不一致性，我们提出一种基于事件的自监督学习图像去噪网络（SEID），通过探索图像噪声统计特性与事件帧间运动信息，为真实场景高质量成像生成人工监督信号。为验证所提NSC的有效性，我们构建了混合相机成像原型系统并在硬件上实现，通过该系统采集涵盖不同目标场景与运动模式的真实世界数据集（包含精确同步的帧与事件）。在合成与真实数据集上的实验表明，我们的方法优于现有最优方案。