Low‑Light Image Enhancement Method Based on ARD‑GAN

JIANG Ze-tao; QIAN Yi; WU Xu; ZHANG Shao‑qin

doi:10.12263/DZXB.20200822

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Low‑Light Image Enhancement Method Based on ARD‑GAN

PAPERS | 更新时间：2025-12-08

- Low‑Light Image Enhancement Method Based on ARD‑GAN
- ACTA ELECTRONICA SINICA Vol. 49, Issue 11, Pages: 2160-2165(2021)
- 作者机构：
  
  1.桂林电子科技大学广西图像图形与智能处理重点实验室，广西桂林 541004
  2.南昌航空大学，江西南昌 330063
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20200822
  CLC： TP391
- Received：03 August 2020，
  
  Revised：2021-01-15，
  
  Published：25 November 2021
- 稿件说明：
移动端阅览
江泽涛,钱艺,伍旭等.一种基于ARD‑GAN的低照度图像增强方法[J].电子学报,2021,49(11):2160-2165.

JIANG Ze-tao,QIAN Yi,WU Xu,et al.Low‑Light Image Enhancement Method Based on ARD‑GAN[J].ACTA ELECTRONICA SINICA,2021,49(11):2160-2165.
江泽涛,钱艺,伍旭等.一种基于ARD‑GAN的低照度图像增强方法[J].电子学报,2021,49(11):2160-2165. DOI： 10.12263/DZXB.20200822.

JIANG Ze-tao,QIAN Yi,WU Xu,et al.Low‑Light Image Enhancement Method Based on ARD‑GAN[J].ACTA ELECTRONICA SINICA,2021,49(11):2160-2165. DOI： 10.12263/DZXB.20200822.

摘要

为解决低照度图像增强过程中噪声放大、细节不足以及色彩还原问题，本文提出一种基于注意力机制残差密集生成对抗网络（Attention Residual Dense‑Generative Adversarial Networks

ARD‑GAN）的低照度图像增强方法.首先

该方法在全局光照估计模块（Global Illumination Estimation Module

GIEM）中生成全局曝光注意力图，以引导后续模块更好地进行照度增强；其次，使用卷积残差模块（Convolution and Residual Module

CRM）和基于通道注意力的残差密集模块（Channel Attention Residual Dense Module

CARDM）分别提取浅层特征和深层特征，并将不同层次的特征融合以获取更好的细节信息；然后，在CARDM基础上将密集连接与批归一化相结合抑制噪声；最后改进了损失函数，使增强后图像色彩还原更好.实验表明，ARD‑GAN有与主流算法相比，在主观视觉和客观评价指标上均得到更好的效果.

Abstract

In order to solve the problems of noise amplification

insufficient detail and color restoration in the process of low‑light image enhancement

this paper proposes a method of low‑light image enhancement based on attention residual dense‑generative adversarial networks (ARD‑GAN). Firstly

the method generates a global exposure attention map in the global illumination estimation module (GIEM) to guide the subsequent modules to enhance the illumination better; secondly

it adopts the convolution and residual module (CRM) and the channel attention residual dense module (CARDM) to extract shallow features and deep features respectively

and fuses different features of levels to obtain better detailed information; furthermore

based on the CARDM

the dense connection and batch normalization are combined to suppress noise. Finally

the improved loss function restores the enhanced image color better. Comprehensive experiments are conducted

which show that ARD‑GAN can significantly outperform mainstream algorithms in subjective vision and objective evaluation indicators.

关键词

Keywords

references

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