Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement
This work addresses the problem of robust low-light enhancement for computational photography and computer vision applications, such as real-time detection and segmentation, with an incremental approach.
The paper tackles low-light image and video enhancement by proposing a semantic-guided zero-shot network (SGZ) that operates without paired data or segmentation annotations, achieving state-of-the-art performance on benchmark datasets.
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