  Low-light images challenge both human perceptions and computer vision
algorithms. It is crucial to make algorithms robust to enlighten low-light
images for computational photography and computer vision applications such as
real-time detection and segmentation. This paper proposes a semantic-guided
zero-shot low-light enhancement network (SGZ) which is trained in the absence
of paired images, unpaired datasets, and segmentation annotation. Firstly, we
design an enhancement factor extraction network using depthwise separable
convolution for an efficient estimate of the pixel-wise light deficiency of an
low-light image. Secondly, we propose a recurrent image enhancement network to
progressively enhance the low-light image with affordable model size. Finally,
we introduce an unsupervised semantic segmentation network for preserving the
semantic information during intensive enhancement. Extensive experiments on
benchmark datasets and a low-light video demonstrate that our model outperforms
the previous state-of-the-art. We further discuss the benefits of the proposed
method for low-light detection and segmentation. Code is available at
https://github.com/ShenZheng2000/Semantic-Guided-Low-Light-Image-Enhancement
