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.

通过模糊噪声权衡，利用非均匀曝光技术在恶劣环境中实现了图像获取的灵活性，进而提出了一种新颖的神经形态快门控制系统 (NSC)，以避免运动模糊和减轻即时噪声，通过利用事件的低延迟性来实时监测运动并实现场景自适应曝光，同时提出了一种基于事件的图像去噪网络 (SEID) 来稳定由非均匀曝光时间引起的信噪比不一致，并通过自监督学习范式探索图像噪声统计和事件的帧间运动信息以获得高质量的实时成像效果。在建立的混合相机成像系统中实现了所提出的 NSC，并收集了包含不同目标场景和运动模式的多样化真实世界数据集，实验结果表明我们的方法优于现有技术。