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Open Access

DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement

College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
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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.

Keywords

Retinexlow-light image enhancementimage decompositionimage adjustment

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Tsinghua Science and Technology
Volume 28 Issue 4,
August 2023
Pages 743-753
DOI: 10.26599/TST.2022.9010047
Cite this article:
Jiang Y, Li L, Zhu J, et al. DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement. Tsinghua Science and Technology, 2023, 28(4): 743-753. https://doi.org/10.26599/TST.2022.9010047

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Received: 21 May 2022
Revised: 10 August 2022
Accepted: 08 October 2022
Published: 06 January 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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