基于生成对抗网络的低光照图像增强算法

杨镇雄; 谭台哲

doi:10.12052/gdutxb.220039

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基于生成对抗网络的低光照图像增强算法

计算机科学与技术 | 更新时间：2026-05-27

- 基于生成对抗网络的低光照图像增强算法
- Low Illumination Image Enhancement Algorithm Based on Generative Adversarial Network
- 广东工业大学学报 2024年41卷第1期页码：55-62
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  杨镇雄(1997–)，男，硕士研究生，主要研究方向为图像处理与计算机视觉，E-mail：yangzx613@foxmail.com
  谭台哲(1970–)，男，副教授，博士，主要研究方向为机器学习与大数据处理、图像处理与计算机视觉、区块链技术，E-mail：taizhetan@gdut.edu.cn
- 基金信息：
- DOI：10.12052/gdutxb.220039
  中图分类号： TP391
- 纸质出版：2024
- 稿件说明：
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杨镇雄, 谭台哲. 基于生成对抗网络的低光照图像增强算法[J]. 广东工业大学学报, 2024, 41(1): 55-62.

Yang Zhen-xiong, Tan Tai-zhe. Low Illumination Image Enhancement Algorithm Based on Generative Adversarial Network[J]. 2024, 41(1): 55-62.
杨镇雄, 谭台哲. 基于生成对抗网络的低光照图像增强算法[J]. 广东工业大学学报, 2024, 41(1): 55-62. DOI： 10.12052/gdutxb.220039.

Yang Zhen-xiong, Tan Tai-zhe. Low Illumination Image Enhancement Algorithm Based on Generative Adversarial Network[J]. 2024, 41(1): 55-62. DOI： 10.12052/gdutxb.220039.

摘要

传统的基于深度学习的方法在低照度图像增强中已经有比较好的发挥，但是这些方法通常需要成对的数据集进行训练，而相对应的数据集正是目前难以收集的。目前的增强方法在真实的低照度图像增强中也会产生增强效果不完美和出现图像噪声等问题。针对这些问题，设计了无监督生成对抗网络，使其可以不用配对训练数据集进行训练，并且把网络分解为注意力机制网络和增强网络2个子网络。通过注意力机制网络把低照度图像中的低光区域和亮光区域区分开，并使用残差增强网络结合全局局部判别器，对图像进行增强。实验结果表明，本文的方法在低光照图像增强方面优于Enlighten-GAN、Cycle-GAN等方法。

Abstract

Traditional deep learning-based methods have achieved promising performance for low-illumination image enhancement. However

these methods usually need to be trained on the pair-wise datasets

which are difficult to collect. Moreover

most existing enhancement methods have the problems of imperfect enhancement effect and image noise in real low illumination image enhancement. To address this

a unsupervised generative adversarial network is designed for low-illumination image enhancement

which has no requirement of training on the pair-wise datasets. The proposed network consists of two subnetworks: attentional mechanism network and enhancement network. The attentional mechanism network is used to distinguish the low-light region from the bright region of the low-illumination image

and the residual enhancement network is used to enhance the image by combining with the global-local discriminator. By doing this

a low-illumination image can be well enhanced. Extensive experimental results show that the proposed method outperforms the baseline Enlighten-GAN and Cycle-GAN for low-light image enhancement.

关键词

Keywords

references

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