  Low-light video enhancement (LLVE) is an important yet challenging task with
many applications such as photographing and autonomous driving. Unlike single
image low-light enhancement, most LLVE methods utilize temporal information
from adjacent frames to restore the color and remove the noise of the target
frame. However, these algorithms, based on the framework of multi-frame
alignment and enhancement, may produce multi-frame fusion artifacts when
encountering extreme low light or fast motion. In this paper, inspired by the
low latency and high dynamic range of events, we use synthetic events from
multiple frames to guide the enhancement and restoration of low-light videos.
Our method contains three stages: 1) event synthesis and enhancement, 2) event
and image fusion, and 3) low-light enhancement. In this framework, we design
two novel modules (event-image fusion transform and event-guided dual branch)
for the second and third stages, respectively. Extensive experiments show that
our method outperforms existing low-light video or single image enhancement
approaches on both synthetic and real LLVE datasets.
