  We focus on a very challenging task: imaging at nighttime dynamic scenes.
Most previous methods rely on the low-light enhancement of a conventional RGB
camera. However, they would inevitably face a dilemma between the long exposure
time of nighttime and the motion blur of dynamic scenes. Event cameras react to
dynamic changes with higher temporal resolution (microsecond) and higher
dynamic range (120dB), offering an alternative solution. In this work, we
present a novel nighttime dynamic imaging method with an event camera.
Specifically, we discover that the event at nighttime exhibits temporal
trailing characteristics and spatial non-stationary distribution. Consequently,
we propose a nighttime event reconstruction network (NER-Net) which mainly
includes a learnable event timestamps calibration module (LETC) to align the
temporal trailing events and a non-uniform illumination aware module (NIAM) to
stabilize the spatiotemporal distribution of events. Moreover, we construct a
paired real low-light event dataset (RLED) through a co-axial imaging system,
including 64,200 spatially and temporally aligned image GTs and low-light
events. Extensive experiments demonstrate that the proposed method outperforms
state-of-the-art methods in terms of visual quality and generalization ability
on real-world nighttime datasets. The project are available at:
https://github.com/Liu-haoyue/NER-Net.
