[1]杨钒,钱立志,刘晓.塔型分解多源图像融合方法[J].计算机技术与发展,2018,28(12):171-175.[doi:10.3969/j.issn.1673-629X.2018.12.036]
　YANG Fan,QIAN Li-zhi,LIU Xiao.A Multi-source Image Fusion Method of Tower Type Decomposition[J].,2018,28(12):171-175.[doi:10.3969/j.issn.1673-629X.2018.12.036]
点击复制

塔型分解多源图像融合方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年12期
页码:
171-175
栏目:
应用开发研究
出版日期:
2018-12-10

文章信息/Info

Title:
A Multi-source Image Fusion Method of Tower Type Decomposition
文章编号:
1673—629X(2018)12—0171—05
作者:
杨钒;  钱立志;  刘晓
陆军军官学院,安徽合肥,230031
Author(s):
YANG Fan; QIAN Li-zhi; LIU Xiao
Army Officer Academy of PLA，Hefei 230031，China
关键词:
图像融合;  金字塔分解;  多源图像;  红外;  微光;  区域融合
Keywords:
image fusion; pyramid decomposition; multi—source image; infrared; low light level; region merging
分类号:
TP911.73
DOI:
10.3969/j.issn.1673-629X.2018.12.036
摘要:
针对红外与微光等不同设备在特定环境下各自的成像效果存在的不足,研究了红外与微光多源图像融合技术.对多源图像常用的融合方法尤其是基于金字塔分解的融合方法进行了研究,分析了图像融合中的塔形分解优点,提出了利用塔型分解图像融合的规则与流程,并分别对基于单个像素和基于像素区域的融合方式进行了详细的描述.通过实验研究了不同分解类型、不同分解层数目及不同融合方式对红外与微光图像融合效果的影响.实验结果表明,基于塔形分解的红外与微光图像融合方法在提高源图像可视度、信息熵、标准差以及平均梯度等图像指标方面可以明显改善图像的融合效果,在保留图像背景信息的同时,能够最大限度地反映和突出图像中的目标信息,有助于后续的目标检测与识别.
Abstract:
Aiming at the deficiency of the infrared and low light level imaging results under cemtin circumstances，we research the infrared and low light level image fusion technology．We study the common fusion methods of multi-source images,especially those based on pyramid decomposition，analyze the advantages of tower decomposition in image fusion，propose the rules and processes of image fusion based on tower decomposition，and describe the fusion methods based on single pixel and pixel area in detail．The effects of different decomposition types，different decomposition layers and different fusion modes on the fusion effect of infrared and low light level images are studied through experiments．It shows that the infrared and low light levelimage fusion method based on pyramid decomposition Can obviously improve image fusion effect in enhancement of image indicators like visibility，information entropy，standard deviation，and average gradient，and reflect and highlight target information of image extremely while re,taining image background information at the same time，which is helpful for the subsequent target detection and recognition．

相似文献/References:

[1]杨亚 王铮 张素兰 郭飞飞.基于小波变换的多聚焦图像融合[J].计算机技术与发展,2010,(03):56.
　YANG Ya,WANG Zheng,ZHANG Su-lan,et al.Multi - focus Image Fusion Scheme Based on Wavelet Transform[J].,2010,(12):56.
[2]冉柯柯 王继成.基于比值法图像拼接的等比例改进算法[J].计算机技术与发展,2010,(02):5.
　RAN Ke-ke,WANG Ji-cheng.An Improved Mosaic Algorithm Based on Ratio Matching Using Geometric Proportion[J].,2010,(12):5.
[3]焦晶萍 廖文和 沈建新.一种基于模板匹配法的眼底图像拼接方法[J].计算机技术与发展,2010,(04):148.
　JIAO Jing-ping,LIAO Wen-he,SHEN Jian-xin.A Fundus Image Mosaic Method Based on Template Matching[J].,2010,(12):148.
[4]玄立超 谢亦才.一种新的基于Curvelet变换的遥感图像融合算法[J].计算机技术与发展,2009,(05):119.
　XUAN Li-chao,XIE Yi-cai.A New Remote Sensing Image Fusion Algorithm Based on Curvelet Transform[J].,2009,(12):119.
[5]田闯 刘文波.基于Curvelet变换多聚焦图像融合[J].计算机技术与发展,2008,(07):29.
　TIAN Chuang,LIU Wen-bo.Multifocus Image Fusion Using Curvelet Transform[J].,2008,(12):29.
[6]杨晓慧 朱秀阁.基于非下采样Contourlets的CT／MRI图像自适应融合[J].计算机技术与发展,2008,(12):116.
　YANG Xiao-hui,ZHU Xiu-ge.Adaptive CT/MRI Image Fusion Based on Nonsubsampled Contourlets[J].,2008,(12):116.
[7]刘凯峰 张德祥.基于小波变换区域方差的遥感图像融合新算法[J].计算机技术与发展,2007,(05):177.
　LIU Kai-feng,ZHANG De-xiang.A New Fusion Algorithm of Remote Sensing Image Based on Local Deviation of Wavelet Transform[J].,2007,(12):177.
[8]舒坚 胡茂林.空间频率图像的智能融合[J].计算机技术与发展,2006,(03):37.
　SHU Jian,HU Mao-lin.Intelligent Fusion of Image in Spatial Frequency[J].,2006,(12):37.
[9]冯太平 闫仁武.基于非抽样Contourlet变换的多聚焦图像融合算法[J].计算机技术与发展,2012,(02):57.
　FENG Tai-ping,YAN Ren-wu.Multi-Focus Image Fusion Algorithm Based on Nonsubsampled Contourlet Transform[J].,2012,(12):57.
[10]魏世超 段先华 夏加星.基于Sobel算子和局部能量的图像融合新算法[J].计算机技术与发展,2012,(04):61.
　WEI Shi-chao,DUAN Xian-hua,XIA Jia-xing.Novel Image Fusion Algorithm Based on Sobel Operator and Region Energy[J].,2012,(12):61.

更新日期/Last Update: 2018-12-10