Low-Light Image Enhancement Method Based on U-Net Generative Adversarial Network

JIANG Ze-tao; QIN Lu-lu

doi:10.3969/j.issn.0372-2112.2020.02.005

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Low-Light Image Enhancement Method Based on U-Net Generative Adversarial Network

更新时间：2025-07-16

- Low-Light Image Enhancement Method Based on U-Net Generative Adversarial Network
- Acta Electronica Sinica Vol. 48, Issue 2, Pages: 258-264(2020)
- 作者机构：
  
  桂林电子科技大学广西图像图形处理与智能处理重点实验室,广西,桂林,541004
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61572147, No.61876049, No.61762066);Science and Technology Project of Guangxi Zhuang Autonomous Region (No.AC16380108);Guangxi Key Laboratory of Image and Graphic and Intelligent Processing (No.GIIP201701, No.GIIP201801, No.GIIP201802, No.GIIP201803);Postgraduate Education Innovation Plan of Guangxi Province (No.YCBZ2018052, No.2019YCXS043)
- DOI：10.3969/j.issn.0372-2112.2020.02.005
  CLC： TP391
- Published Online：25 February 2020，
  
  Published：2020
- 稿件说明：
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JIANG Ze-tao, QIN Lu-lu. Low-Light Image Enhancement Method Based on U-Net Generative Adversarial Network[J]. Acta Electronica Sinica, 2020, 48(2): 258-264.
DOI：

JIANG Ze-tao, QIN Lu-lu. Low-Light Image Enhancement Method Based on U-Net Generative Adversarial Network[J]. Acta Electronica Sinica, 2020, 48(2): 258-264. DOI： 10.3969/j.issn.0372-2112.2020.02.005.

摘要

在低照度环境下采集的图像具有低信噪比、低对比度及低分辨率等特点，导致图像难以识别利用.为了提升低照度图像的质量，本文提出一种基于U-Net生成对抗网络的低照度图像增强方法.首先利用U-Net框架实现生成对抗网络中的生成网络，然后利用该生成对抗网络学习从低照度图像到正常照度图像的特征映射，最终实现低照度图像的照度增强.实验结果表明，与主流算法相比，本文提出的方法能够更有效的提升低照度图像的亮度与对比度.

Abstract

The images acquired in the low illumination environment have the characteristics of low signal-to-noise ratio

low contrast and low resolution

which make the image difficult to identify and utilize.In order to improve the image quality of low-light images

this paper proposes a low-light image enhancement method based on U-net generative adversarial network (GAN). First

the U-net framework is used to implement the generative network of GAN

and then the GAN is used to learn the feature mapping from the low-light image to the normal-light image

and ultimately achieve illumination enhancement for the low-light image. Finally

this method is verified by experiments. The experimental results show that

compared with the mainstream algorithm

the proposed algorithm can effectively improve the brightness and contrast of low-light image.

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