  Event camera has recently received much attention for low-light image
enhancement (LIE) thanks to their distinct advantages, such as high dynamic
range. However, current research is prohibitively restricted by the lack of
large-scale, real-world, and spatial-temporally aligned event-image datasets.
To this end, we propose a real-world (indoor and outdoor) dataset comprising
over 30K pairs of images and events under both low and normal illumination
conditions. To achieve this, we utilize a robotic arm that traces a consistent
non-linear trajectory to curate the dataset with spatial alignment precision
under 0.03mm. We then introduce a matching alignment strategy, rendering 90% of
our dataset with errors less than 0.01s. Based on the dataset, we propose a
novel event-guided LIE approach, called EvLight, towards robust performance in
real-world low-light scenes. Specifically, we first design the multi-scale
holistic fusion branch to extract holistic structural and textural information
from both events and images. To ensure robustness against variations in the
regional illumination and noise, we then introduce a Signal-to-Noise-Ratio
(SNR)-guided regional feature selection to selectively fuse features of images
from regions with high SNR and enhance those with low SNR by extracting
regional structure information from events. Extensive experiments on our
dataset and the synthetic SDSD dataset demonstrate our EvLight significantly
surpasses the frame-based methods. Code and datasets are available at
https://vlislab22.github.io/eg-lowlight/.
