基于过滤—群聚注意力的低光增强递归网络

黄志雄; 李晋江; 华臻; 范琳伟

doi:10.1631/FITEE.2200344

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基于过滤—群聚注意力的低光增强递归网络

常规文章 | Updated：2023-07-24

- 基于过滤—群聚注意力的低光增强递归网络
- Filter-cluster attention based recursive network for low-light enhancement
- 信息与电子工程前沿（英文） 2023年24卷第7期页码：1028-1044
- Affiliations：
  
  1.School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China
  2.School of Computer Science and Technology, Shandong Technology and Business University, Yantai 264005, China
  3.Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai 264005, China
  4.School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China
- Author bio：
  
  †E-mail: hzxcyanwind@163.com
  ‡Corresponding author
- Funds：
  
  National Natural Science Foundation of China(61772319;62002200;62202268);Shandong Natural Science Foundation of China(ZR2021QF134;ZR2021MF107);Shandong Provincial Science and Technology Support Program for Youth Innovation Team in Colleges(2021KJ069;2019KJN042);Yantai Science and Technology Innovation Development Plan(2022JCYJ031)
- DOI：10.1631/FITEE.2200344
  中图分类号： TP391
- 收稿：2022-08-11，
  
  录用：2022-11-28，
  
  纸质出版：2023-07-0
- Accepted：
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黄志雄, 李晋江, 华臻, 等. 基于过滤—群聚注意力的低光增强递归网络[J]. 信息与电子工程前沿（英文）, 2023,24(7):1028-1044.

Zhixiong HUANG, Jinjiang LI, Zhen HUA, et al. Filter-cluster attention based recursive network for low-light enhancement[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 1028-1044.
黄志雄, 李晋江, 华臻, 等. 基于过滤—群聚注意力的低光增强递归网络[J]. 信息与电子工程前沿（英文）, 2023,24(7):1028-1044. DOI： 10.1631/FITEE.2200344.

Zhixiong HUANG, Jinjiang LI, Zhen HUA, et al. Filter-cluster attention based recursive network for low-light enhancement[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 1028-1044. DOI： 10.1631/FITEE.2200344.

摘要

在低光环境下拍摄的图像质量不佳，影响其进一步应用。为提升低光图像可视性，提出一种基于过滤—群聚注意力（FCA）的递归网络，其中主体由3个单元组成：差异关注、门控递归以及迭代残差。该网络对低光图像进行多阶段递归学习，进而提取更深层次特征信息。为算得更加精确的相关性，设计了一种关注特征通道突出性的FCA。FCA与自注意力被用以突出特征的低光区域与重要通道。此外，设计了密集连接金字塔（DenCP）来提取低光反转图的色彩特征，使图像的色彩信息损失得以补偿。在6种公开数据集上的实验结果表明，本文方法在视觉和指标上有着突出表现。

Abstract

The poor quality of images recorded in low-light environments affects their further applications. To improve the visibility of low-light images

we propose a recurrent network based on filter-cluster attention (FCA)

the main body of which consists of three units: difference concern

gate recurrent

and iterative residual. The network performs multi-stage recursive learning on low-light images

and then extracts deeper feature information. To compute more accurate dependence

we design a novel FCA that focuses on the saliency of feature channels. FCA and self-attention are used to highlight the low-light regions and important channels of the feature. We also design a dense connection pyramid (DenCP) to extract the color features of the low-light inversion image

to compensate for the loss of the image’s color information. Experimental results on six public datasets show that our method has outstanding performance in subjective and quantitative comparisons.

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