Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 5
1579-1585

PNet:multi-level low-illumination image enhancement network based on attention mechanism

Yang Wei1,2a,2b
Zhang Zhiwei1
Cheng Haixiu2a,2c
1. Dept. of Software Engineering, Software Engineering Institute of Guangzhou, Guangzhou 510990, China
2. a. School of Computer Science & Engineering, b. Machine Learning & Data Mining Team, c. Guangdong Province Computer Network Key Laboratory, South China University of Technology, Guangzhou 510641, China
DOI: 10.19734/j.issn.1001-3695.2021.09.0384

Abstract

Low-illumination image has degradation problems such as low brightness, noise artifact, detail loss and color distortion, which makes it a multiobjective task of the low-illumination image enhancement. As most existing enhancement algorithms fail to provide comprehensive performance in enhancing multiple targets, this paper proposed a model——PNet: multi-level low-illumination image enhancement network based on attention mechanism, which built a multi-stage tandem enhancement task subnet, and designed a multi-channel information fusion module for effective feature selection and memory with attention mechanism. With it, the network could process the image stream in a sequential manner, and collaboratively and incrementally completed multi-tasks such as the global brightness adaptive image enhancement, noise and artifact suppression, detail restoration, and color correction. In addition, through quantitative and qualitative comparison with existing mainstream algorithms, it showed that the proposed method could achieve brightness adaptive image enhancement and detail contrast enhancement. The enhanced image had an overall natural brightness with no obvious halo and artifacts on the one hand, and the color of which was rich and true on the other. Besides, compared with the sub-optimal algorithm, the index of the PSNR, SSIM and RMSE of the images processed by the proposed model was increased by 0.229, 0.112, and 0.335. Experiment results show that the proposed method achieves excellent performance in multi-objective task of the low-illumination image enhancement, which has certain value in practice.

Foundation Support

2018年度广东省普通高校重点科研平台和科研项目(2018KQNCX395, 2018KQNCX394)
2021年度广东省普通高校特色创新(自然科学)项目(2021KTSCX160,2021KTSCX161)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.09.0384
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 5
Section: Technology of Graphic & Image
Pages: 1579-1585
Serial Number: 1001-3695(2022)05-051-1579-07

Publish History

[2021-11-22] Accepted Paper
[2022-05-05] Printed Article

Cite This Article

杨微, 张志威, 成海秀. PNet：融合注意力机制的多级低照度图像增强网络 [J]. 计算机应用研究, 2022, 39 (5): 1579-1585. (Yang Wei, Zhang Zhiwei, Cheng Haixiu. PNet:multi-level low-illumination image enhancement network based on attention mechanism [J]. Application Research of Computers, 2022, 39 (5): 1579-1585. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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