  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.
