PEN-DS: progressive enhancement network based on detail supplementation for low-light image enhancement

Yong Yang, Wenzhi Xu, Shuying Huang, Weiguo Wan

Abstract: Images captured in low-light environments suffer from severe degradation, which can be unfavorable for human observation and subsequent computer vision tasks. Although many enhancement methods based on deep learning have been proposed, the obtained enhancement images still suffer from drawbacks such as color distortion, noise, and blur. To solve these problems, we propose a progressive enhancement network based on detail supplementation (PEN-DS), which is implemented by building two modules: an image preprocessing module (IPM) and a progressive image enhancement module (PIEM). The IPM can obtain low-light images and low-detail maps at different scales by building an image pyramid structure. PIEM can enhance images at different scales progressively based on detail supplementation and luminance enhancement. In addition, to better train the network, the proposed method employs a multi-supervised joint loss function for the enhanced images of different scales. Experimental results show that the proposed method outperforms state-of-the-art approaches in terms of visual observation and objective evaluation.