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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.


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

Show Author's information Yonglong Jiang1Liangliang Li2Jiahe Zhu2Yuan Xue1Hongbing Ma2( )
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

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:

Retinex, low-light image enhancement, image decomposition, image adjustment
Received: 21 May 2022 Revised: 10 August 2022 Accepted: 08 October 2022 Published: 06 January 2023 Issue date: August 2023
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Publication history

Received: 21 May 2022
Revised: 10 August 2022
Accepted: 08 October 2022
Published: 06 January 2023
Issue date: August 2023

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© The author(s) 2023.

Acknowledgements

This work was supported by the Shanghai Aerospace Science and Technology Innovation Fund (No. SAST2019-048) and the Cross-Media Intelligent Technology Project of Beijing National Research Center for Information Science and Technology (BNRist) (No. BNR2019TD01022).

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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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