  Although synthetic aperture imaging (SAI) can achieve the seeing-through
effect by blurring out off-focus foreground occlusions while recovering
in-focus occluded scenes from multi-view images, its performance is often
deteriorated by dense occlusions and extreme lighting conditions. To address
the problem, this paper presents an Event-based SAI (E-SAI) method by relying
on the asynchronous events with extremely low latency and high dynamic range
acquired by an event camera. Specifically, the collected events are first
refocused by a Refocus-Net module to align in-focus events while scattering out
off-focus ones. Following that, a hybrid network composed of spiking neural
networks (SNNs) and convolutional neural networks (CNNs) is proposed to encode
the spatio-temporal information from the refocused events and reconstruct a
visual image of the occluded targets. Extensive experiments demonstrate that
our proposed E-SAI method can achieve remarkable performance in dealing with
very dense occlusions and extreme lighting conditions and produce high-quality
images from pure events. Codes and datasets are available at
https://dvs-whu.cn/projects/esai/.

Computer Vision and Pattern Recognition