@article{Jiang2023, 
author = {Yonglong Jiang and Liangliang Li and Jiahe Zhu and Yuan Xue and Hongbing Ma},
title = {DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement},
year = {2023},
journal = {Tsinghua Science and Technology},
volume = {28},
number = {4},
pages = {743-753},
keywords = {Retinex, low-light image enhancement, image decomposition, image adjustment},
url = {https://www.sciopen.com/article/10.26599/TST.2022.9010047},
doi = {10.26599/TST.2022.9010047},
abstract = {Poor illumination greatly affects the quality of obtained images. In this paper, a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement. DEANet combines the frequency and content information of images and is divided into three subnetworks: decomposition, enhancement, and adjustment networks, which perform image decomposition; denoising, contrast enhancement, and detail preservation; and image adjustment and generation, respectively. The model is trained on the public LOL dataset, and the experimental results show that it outperforms the existing state-of-the-art methods regarding visual effects and image quality.}
}