基于FPDE的红外与可见光图像融合算法

高雪琴; 刘刚; 肖刚; BAVIRISETTIDurga Prasad; 史凯磊

doi:10.16383/j.aas.2018.c180188

基于FPDE的红外与可见光图像融合算法

doi: 10.16383/j.aas.2018.c180188

1.
上海电力学院自动化工程学院上海 200090
2.
上海交通大学航空航天学院上海 200240

基金项目:

国家重点基础研究发展计划(973计划) 2014CB744903

国家自然科学基金 61673270

国家自然科学基金 61203224

上海浦江人才计划 16PJD028

详细信息

作者简介:
高雪琴上海电力学院自动化工程学院硕士研究生.主要研究方向为图像融合. E-mail: gxq0163@163.com

肖刚上海交通大学航空航天学院教授. 2004年获得上海交通大学博士学位.主要研究方向为先进航空电子综合仿真, 智能信息处理.E-mail:xiaogang@sjtu.edu.cn

BAVIRISETTIDurga Prasad：BAVIRISETTI Durga Prasad 上海交通大学航空航天学院博士后. 2016年获得印度韦洛尔科技大学博士学位.主要研究方向为图像融合, 目标跟踪, 机器学习和计算机视觉. E-mail:bdps1989@sjtu.edu.cn

史凯磊上海电力学院自动化工程学院硕士研究生.主要研究方向为机器视觉. E-mail: 18321779651@163.com

通讯作者:
刘刚上海电力学院自动化工程学院教授. 2005年获得上海交通大学博士学位.主要研究方向为模式识别, 机器学习.本文通信作者. E-mail: liugang@shiep.edu.cn

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出版历程
- 收稿日期: 2018-04-02
- 录用日期: 2018-09-12
- 刊出日期: 2020-04-24

Fusion Algorithm of Infrared and Visible Images Based on FPDE

1.
School of Automation Engineering, Shanghai University of Electrical Power, Shanghai 200090
2.
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240

Funds:

National Basic Research Program of China (973 Program) 2014CB744903

National Natural Science Foundation of China 61673270

National Natural Science Foundation of China 61203224

Shanghai Pujiang Talent Program 16PJD028

More Information

Author Bio:
GAO Xue-Qin Master student at the School of Automation Engineering, Shanghai University of Electrical Power, China. Her main research interest is image fusion

XIAO Gang Professor at the School of Aeronautics and Astronautics, Shanghai Jiao Tong University, China. He received his Ph. D. degree from Shanghai Jiao Tong University, China in 2004. His research interest covers advanced avionics integrated simulation and intelligent information processing

BAVIRISETTI Durga Prasad Postdoctor at the School of Aeronautics and Astronautics, Shanghai Jiao Tong University. He received his Ph. D. degree from Vellore Institute of Technology, India. His research interest covers image fusion, target tracking, machine learning, and computer vision

SHI Kai-Lei Master student at the School of Automation Engineering, Shanghai University of Electrical Power, China. His main research interest is machine vision

Corresponding author: LIU Gang Professor at the School of Automation Engineering, Shanghai University of Electrical Power, China. He received his Ph. D. degree from Shanghai Jiao Tong University, China in 2005. His research interest covers pattern recognition and machine learning. Corresponding author of this paper

摘要

摘要: 针对传统红外与可见光图像融合算法中存在的细节信息不够丰富, 边缘信息保留不够充分等问题, 文中提出了一种基于四阶偏微分方程(Fourth-order partial differential equation, FPDE)的改进的图像融合算法.算法首先采用FPDE将已配准的红外与可见光图像进行分解, 得到高频分量和低频分量; 然后, 对高频分量采用基于主成分分析(Principal component analysis, PCA)的融合规则来得到细节图像, 对低频分量采用基于期望值最大(Expectation maximization, EM)的融合规则来得到近似图像; 最后, 通过组合最终的高频分量和低频分量来重构得到最终的融合结果.实验是建立在标准的融合数据集上进行的, 并与传统的和最近的融合方法进行比较, 结果证明所提方法得到的融合图像比现有的融合方法能有效地综合红外与可见光图像中的重要信息, 有更好的视觉效果.
- 图像融合 /
- 四阶偏微分方程 /
- 期望值最大 /
- 主成分分析 /
- 红外与可见光图像
Abstract: Aiming at the problems that the traditional infrared and visible light image fusion algorithms are not rich enough in detail, and the edge information is not sufficiently reserved, an improved image fusion algorithm based on the fourth-order partial differential equation (FPDE) is proposed. The algorithm firstly decomposes the registered infrared and visible images with FPDE to get the high frequency and low frequency components, and then uses the principal component analysis (PCA) fusion rules for the high frequency components to obtain the detail image. An approximation image is obtained using the fusion rule based on the expectation maximization (EM) for low frequency components; Finally, by combining the final high-frequency components and low frequency components to reconstruct the final fusion results. The experiment is based on a standard fusion dataset and compared with the traditional and recent fusion methods. The results show that the proposed fusion method can effectively integrate the important information of infrared and visible images, better visual effects.
- Image fusion /
- fourth-order partial differential equation(FPDE) /
- expectation maximization (EM) /
- principal component analysis(PCA) /
- infrared and visible images
Recommended by Associate Editor HUANG Qing-Ming
注释:

1) 本文责任编委黄庆明

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图 1 本文算法的流程图

Fig. 1 Flow chart of the algorithm of this paper

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图 2 "dune"图像的融合结果

Fig. 2 The fusion result of image "dune"

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图 3 "pavilion"图像的融合结果

Fig. 3 The fusion result of image "pavilion"

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图 4 "maninhuis"图像的融合结果

Fig. 4 The fusion result of image "maninhuis"

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图 5 "UN Camp"图像的融合结果

Fig. 5 The fusion result of image "UN Camp"

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图 6 其他6组图像的融合结果

Fig. 6 The fusion result of other six groups of images

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表 1 "dune"的融合结果的客观指标评价结果

Table 1 Objective evaluation result of the fusion result of "dune"

融合方法	MI	IE	SD	EIPV
GRAD	1.2948	4.9	37.9309	0.4278
RATIO	0.7248	4.2485	18.5883	0.3546
DWT	0.6542	4.3821	21.0136	0.3841
FPDE	0.7339	4.2997	19.6550	0.4821
CVT-SR	1.5809	4.8953	38.4630	0.4408
VSM	0.7053	4.3278	20.1876	0.4085
本文方法	2.4647	5.0006	41.2892	0.5106

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表 2 "pavilion"的融合结果的客观指标评价结果

Table 2 Objective evaluation result of the fusion result of "pavilion"

融合方法	MI	IE	SD	EIPV
GRAD	1.9984	4.4074	26.9211	0.4625
RATIO	1.2350	4.4073	25.5996	0.3724
DWT	1.1351	4.4671	28.2542	0.4197
FPDE	1.3621	4.3829	25.8059	0.3846
CVT-SR	1.3812	5.2110	48.8611	0.4251
VSM	1.2409	4.4436	27.8868	0.4451
本文方法	3.1240	4.8207	59.5605	0.4671

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表 3 "maninhuis"的融合结果的客观指标评价结果

Table 3 Objective evaluation result of the fusion result of "maninhuis"

融合方法	MI	IE	SD	EIPV
GRAD	1.6169	4.7116	28.0854	0.4814
RATIO	0.9408	4.6744	26.7289	0.3857
DWT	0.9312	4.7941	30.4888	0.4371
FPDE	1.0689	4.7081	27.4131	0.4516
CVT-SR	1.3302	5.151	46.7314	0.5015
VSM	1.0296	4.760	29.3688	0.4921
本文方法	3.2612	5.036	57.1928	0.5091

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表 4 "UN Camp"的融合结果的客观指标评价结果

Table 4 Objective evaluation result of the fusion result of "UN Camp"

融合方法	MI	IE	SD	EIPV
GRAD	1.3059	4.8571	36.0841	0.3918
RATIO	1.0455	4.3359	22.9454	0.3895
DWT	1.0314	4.4535	24.9950	0.3641
FPDE	1.0894	4.3503	22.9777	0.4234
CVT-SR	1.1538	4.7549	32.5279	0.3635
VSM	1.0753	4.3991	23.9920	0.4064
本文方法	2.5529	4.9069	41.5711	0.4461

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表 5 其他6组图像客观评价结果

Table 5 Objective evaluation result of the other six groups of images

融合方法	GRAD	RATIO	DWT	FPDE	CVT-SR	VSM	本文方法
MI	1.9322	1.3495	1.2301	1.3235	1.4803	1.3201	2.9112
IE	4.9659	4.3952	4.4904	4.4409	4.8827	4.4594	4.9711
SD	49.5321	23.9142	25.5283	24.3822	41.2654	26.6849	51.8051
EIPV	0.4732	0.3491	0.4651	0.5244	0.4653	0.4857	0.5276

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表 6 各种融合方法的计算时间对比

Table 6 Computational time comparison of different fusion methods

融合方法	GRAD	RATIO	DWT	FPDE	CVT-SR	VSM	本文方法
时间(s)	2.7914	0.6909	1.6567	8.1072	5.4289	5.6316	71.7869

下载: 导出CSV

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