[1]赵立昌,张宝辉,吴 杰,等.基于灰度能量差异性的红外与可见光图像融合[J].红外技术,2020,42(8):775-782.[doi:10.11846/j.issn.1001_8891.202008012]
 ZHAO Lichang,ZHANG Baohui,WU Jie,et al.Fusion of Infrared and Visible Images Based on Gray Energy Difference[J].Infrared Technology,2020,42(8):775-782.[doi:10.11846/j.issn.1001_8891.202008012]
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基于灰度能量差异性的红外与可见光图像融合
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
42卷
期数:
2020年第8期
页码:
775-782
栏目:
出版日期:
2020-08-23

文章信息/Info

Title:
Fusion of Infrared and Visible Images Based on Gray Energy Difference
文章编号:
1001-8891(2020)08-0775-08
作者:
赵立昌1张宝辉2吴 杰2吴旭东2吉 莉2
1. 南京理工大学 电子工程与光电技术学院,江苏 南京 210094; 2. 昆明物理研究所,云南 昆明 650223
Author(s):
ZHAO Lichang 1ZHANG Baohui2WU Jie2WU Xudong2JI Li2
 1. School of Elect ronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China ; 2. Kunming Institute of Physics, Kunming 650223, China
关键词:
图像融合灰度能量差异NSCT加权方差
Keywords:
image fusion gray energy difference NSCT weighted variance
分类号:
TP391
DOI:
10.11846/j.issn.1001_8891.202008012
文献标志码:
A
摘要:
为了获取红外图像中的突出的目标特征,提取可见光图像中重要的细节信息,以及解决传统算法中目标信息不够突出,细节、纹理缺失严重的问题,本文提出了一种基于灰度能量差异性的红外与可见光图像融合方法。首先通过基于灰度能量差异性的显著目标提取算法检测出红外图像中的目标特征;然后采用非下采样轮廓波变换(non-subsampled contourlet transform,NSCT)对红外图像和可见光图像进行高低频的分解;将灰度能量差异图作为融合权重对红外图像和可见光图像的低频部分进行融合,对于高频部分采用加权方差的规则进行融合;最后对融合后的高频系数和低频系数进行NSCT逆变换得到最终的融合图像。本文选取了3组经典的红外与可见光图像进行融合实验,并且通过主观视觉和客观指标两个方面与其他几种方法作比较。实验结果证明了算法在突出目标信息、提高对比度、清晰度和保留纹理细节方面十分有效。
Abstract:
This paper proposes an infrared and visible image fusion method based on gray energy difference for two purposes: one, to obtain the prominent target features in an infrared image for extracting the important details in the visible image, and two, to solve the problem that the target information in traditional algorithms is not sufficiently prominent and that the details and textures are often missing. In this method, first, the target feature in the infrared image is detected by a target extraction algorithm based on gray energy difference. Second, infrared and visible images are decomposed to high and low frequencies using a non-subsampled contourlet transform (NSCT). Third, the gray energy difference map is used as the fusion weight to fuse the low-frequency parts of the infrared image and the visible image. The high-frequency part is fused by the rule of weighted variance. Finally, the NSCT inverse transform is used to fuse the high-frequency and low-frequency coefficients to obtain the final fused image. In this study, three groups of classical infrared and visible images are selected for fusion experiments and compared with other methods through subjective vision and objective indicators. Experimental results show that the algorithm can effectively highlight target information, improving contrast and sharpness and retaining texture details.

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备注/Memo

备注/Memo:
收稿日期:2019-11-29;修订日期:2020-01-08.
作者简介:赵立昌(1996-),男,硕士,主要从事红外图像处理技术的研究。E-mail:zhaolichang_njust@163.com。
通信作者:张宝辉(1984-),男,博士,高级工程师,主要从事红外探测与图像融合的研究。E-mail:zbhmatt@163.com。
基金项目:国家重点研发计划“基于自由曲面的共体光学系统纳米精度制造基础研究”项目(2017YFA0701200)。
更新日期/Last Update: 2020-08-19