基于稀疏补算子学习的图像融合方法

doi:10.13229/j.cnki.jdxbgxb201606039

吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (6): 2052-2058.doi: 10.13229/j.cnki.jdxbgxb201606039

基于稀疏补算子学习的图像融合方法

尹明^{1, 2}, 战荫伟³, 裴海龙²

1.广东工业大学自动化学院,广州 510006;
2.华南理工大学自主系统与网络控制教育部重点实验室,广州 510641;
3.广东工业大学计算机学院,广州 510006

收稿日期:2016-03-22 出版日期:2016-11-20 发布日期:2016-11-20
通讯作者: 战荫伟(1966-),男,教授,博士.研究方向:图像处理.E-mail:ywzhan@gdut.edu.cn
作者简介:尹明(1975-),男,副教授,博士.研究方向:机器学习、模式识别及图像处理.E-mail:yiming@gdut.edu.cn
基金资助:
广东省自然科学基金面上项目(2014A030313511); 广东省教育厅高等院校学科建设专项基金项目(12ZK0362); 自主系统与网络控制教育部重点实验室开放基金项目(2013A06); 教育部留学回国人员科研启动基金项目; 广东省科技计划项目(2014B040401012)

Co-sparse analysis operator learning for image fusion

YIN Ming^{1, 2}, ZHAN Yin-wei³, PEI Hai-long²

1.School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
2.Key Lab of Autonomous Systems and Networked Control, Ministry of Education, Guangzhou 510641, China;
3.School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China

Received:2016-03-22 Online:2016-11-20 Published:2016-11-20

摘要/Abstract

摘要： 为获得高质量融合图像,本文运用稀疏补分解理论,提出了一种新的多聚焦图像融合方法。首先给出了正则化约束下的稀疏补分解算子学习模型,并从相似样本数据训练出分解算子;再利用分解算子从待融合图像中提取稀疏特征,经取大融合规则获得融合系数;最后通过极小化全变差问题重建融合图像。实验结果表明,本文方法优于稀疏综合表示的图像融合算法:在互信息指标上,最大增益为0.65;在Q_ABF指标上,最大增益为0.1。

关键词: 计算机应用, 稀疏补分解, 特征提取, 稀疏表示, 图像融合, 多聚焦

Abstract: In order to achieve image fusion with high quality, a multi-focus image fusion scheme with co-sparse analysis is proposed. First, a co-sparse analysis operator learning model under regularization is developed, for which the analysis operator is obtained with similarity training data. Then, the images to be fused are represented by the salient features extracted with the analysis operator and their corresponding fusion coefficients are obtained with the choose-max rule. Finally, the fused image is reconstructed by minimizing a total variation norm regularized problem. Experimental results show that the proposed method outperforms the sparse synthesis based fusion method; the gain of the proposed method is up to 0.65 in terms of mutual information and 0.1 in terms of Q_ABF.

Key words: computer application, co-sparse analysis, feature extraction, sparse representation, image fusion, multi-focus

中图分类号:

TP391

尹明, 战荫伟, 裴海龙. 基于稀疏补算子学习的图像融合方法[J]. 吉林大学学报(工学版), 2016, 46(6): 2052-2058.

YIN Ming, ZHAN Yin-wei, PEI Hai-long. Co-sparse analysis operator learning for image fusion[J]. 吉林大学学报(工学版), 2016, 46(6): 2052-2058.

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基于稀疏补算子学习的图像融合方法

Co-sparse analysis operator learning for image fusion

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