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CLC number: TP391

On-line Access: 2023-07-24

Received: 2022-08-11

Revision Accepted: 2023-07-24

Crosschecked: 2022-11-28

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhixiong HUANG

https://orcid.org/0000-0002-2080-8678

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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.7 P.1028-1044

http://doi.org/10.1631/FITEE.2200344


Filter-cluster attention based recursive network for low-light enhancement


Author(s):  Zhixiong HUANG, Jinjiang LI, Zhen HUA, Linwei FAN

Affiliation(s):  School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China; more

Corresponding email(s):   hzxcyanwind@163.com, lijinjiang@gmail.com-

Key Words:  Low-light enhancement, Filter-cluster attention, Dense connection pyramid, Recursive network


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

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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.

基于过滤-群聚注意力的低光增强递归网络

黄志雄1,3,李晋江2,3,华臻1,3,范琳伟4
1山东工商学院信息与电子工程学院,中国烟台市,264005
2山东工商学院计算机科学与技术学院,中国烟台市,264005
3山东工商学院山东省高等学校未来智能计算协同创新中心,中国烟台市,264005
4山东财经大学计算机科学与技术学院,中国济南市,250014
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。

关键词:低光增强;过滤-群聚注意力;密集连接金字塔;递归网络

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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