TY - JOUR AU - Jiang, Yonglong AU - Li, Liangliang AU - Zhu, Jiahe AU - Xue, Yuan AU - Ma, Hongbing PY - 2023 TI - DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement JO - Tsinghua Science and Technology SN - 1007-0214 SP - 743 EP - 753 VL - 28 IS - 4 AB - 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. UR - https://doi.org/10.26599/TST.2022.9010047 DO - 10.26599/TST.2022.9010047