  Low-light imaging with handheld mobile devices is a challenging issue.
Limited by the existing models and training data, most existing methods cannot
be effectively applied in real scenarios. In this paper, we propose a new
low-light image restoration method by using the complementary information of
short- and long-exposure images. We first propose a novel data generation
method to synthesize realistic short- and longexposure raw images by simulating
the imaging pipeline in lowlight environment. Then, we design a new
long-short-exposure fusion network (LSFNet) to deal with the problems of
low-light image fusion, including high noise, motion blur, color distortion and
misalignment. The proposed LSFNet takes pairs of shortand long-exposure raw
images as input, and outputs a clear RGB image. Using our data generation
method and the proposed LSFNet, we can recover the details and color of the
original scene, and improve the low-light image quality effectively.
Experiments demonstrate that our method can outperform the state-of-the art
methods.
