AFCANet: An adaptive feature concatenate attention network for multi-focus image fusion

doi:10.1016/j.jksuci.2023.101751

	AFCANet: An adaptive feature concatenate attention network for multi-focus image fusion
	Liu, Shuaiqi 1,2,3; Peng, Weijian 1,2; Liu, Yali 1,2; Zhao, Jie 1,2; Su, Yonggang 1,2; Zhang, Yudong 4
发表期刊	JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
ISSN	1319-1578
	2023-10-01
卷号	35 期号:9 页码:22
通讯作者	Su, Yonggang(ygsu0726@163.com) ; Zhang, Yudong()
摘要	For multi-focus image fusion, the existing deep learning based methods cannot effectively learn the texture features and semantic information of the source image to generate high-quality fused images. Thus, we develop a new adaptive feature concatenate attention network named AFCANet, which adaptively learns cross-layer features and retains the texture features and semantic information of images to generate visually appealing fully focused images. In AFCANet, the encoder-decoder network is used as the backbone network. In the unsupervised training stage, an adaptive cross-layer skip connection mode is designed, and a cross-layer adaptive coordinate attention module is built to acquire meaningful information from the image along with ignoring unimportant information to obtain a better image fusion effect. In addition, in the middle of the encoder-decoder network, we also introduce an effective channel attention module to fully learn the output of the encoder, and accelerate network convergence. In the inference stage, we apply the pixel-based spatial frequency fusion rules to fuse the adaptive features learned by the encoder, which can successfully combine the texture and semantic information of the image and produce a more precise decision map. Extensive experiments on public datasets and the HBU-CVMDSP dataset show that our AFCANet can effectively improve the accuracy of the decision map in the focus and defocus regions, as well as improve the ability to retain the abundant details and edge features of the source image.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
关键词	Multi -focus image fusion Unsupervised training Adaptive feature concatenate Attention module
DOI	10.1016/j.jksuci.2023.101751
关键词[WOS]	ALGORITHM
收录类别	SCI
语种	英语
资助项目	Natural Science Foun- dation of Hebei Province[F2022201055] ; Natural Science Foun- dation of Hebei Province[2022M713361] ; National Natural Science Foundation of China[62172139] ; Natural Science Foundation of Hebei Province[F2022201055] ; China Postdoctoral[2022M713361] ; Hebei University Research and Innovation Team Support Project[IT2023B05] ; Natural Science Interdisciplinary Research Program of Hebei University[DXK202102] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)[202200007] ; High-Performance Computing Center of Hebei University
项目资助者	Natural Science Foun- dation of Hebei Province ; National Natural Science Foundation of China ; Natural Science Foundation of Hebei Province ; China Postdoctoral ; Hebei University Research and Innovation Team Support Project ; Natural Science Interdisciplinary Research Program of Hebei University ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; High-Performance Computing Center of Hebei University
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:001082388200001
出版者	ELSEVIER
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/52975
专题	多模态人工智能系统全国重点实验室
通讯作者	Su, Yonggang; Zhang, Yudong
作者单位	1.Hebei Univ, Coll Elect & Informat Engn, Baoding 071000, Hebei, Peoples R China 2.Machine Vis Technol Innovat Ctr Hebei Prov, Baoding 071000, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 4.Univ Leicester, Sch Comp & Math Sci, Leicester LE1 7RH, England
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Liu, Shuaiqi,Peng, Weijian,Liu, Yali,et al. AFCANet: An adaptive feature concatenate attention network for multi-focus image fusion[J]. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES,2023,35(9):22.
APA	Liu, Shuaiqi,Peng, Weijian,Liu, Yali,Zhao, Jie,Su, Yonggang,&Zhang, Yudong.(2023).AFCANet: An adaptive feature concatenate attention network for multi-focus image fusion.JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES,35(9),22.
MLA	Liu, Shuaiqi,et al."AFCANet: An adaptive feature concatenate attention network for multi-focus image fusion".JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 35.9(2023):22.