A Semi-Supervised Underexposed Image Enhancement Network With Supervised Context Attention and Multi-Exposure Fusion

Recently, image enhancement approaches yield impressive progress. However, most methods are still based supervised-learning, which requires plenty of paired data. Meanwhile, owing to the complex illumination condition in a real-world scenario, those methods trained on synthetic images cannot restore details in extremely dark or bright areas and lead to exposure errors. The traditional losses that deem all pixels the same in training also produce blurry edges in the result. To handle these problems, in this article, we present an effective semi-supervised framework for severely underexposed image enhancement. Our network consists of a supervised and an unsupervised branch, which shares weights and can make full use of paired data and plenty of unpaired data. Meanwhile, a multi-exposure fusion module is designed to adaptively fuse the corrected images to address the low contrast and color bias issues occurring in some extreme situations. Moreover, we propose a supervised context attention module to better use the edge information as supervision to recover fine image details. Extensive experiments have proved that the proposed method outperforms state-of-the-art approaches in enhancing exposure images.