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.

提出了一种使用短曝光和长曝光图像的互补信息进行低光照条件下的图像修复方法，通过模拟低光环境中的成像管道生成的真实短曝光和长曝光原始图像，并使用新的长短曝光融合网络 (LSFNet) 处理低光照图像融合中的问题，包括高噪声、运动模糊、色彩失真和错位，实验结果表明，该方法优于现有的最优方法。