[1]周晨旭,黄福珍.基于BLMD和NSDFB算法的红外与可见光图像融合方法[J].红外技术,2019,41(2):176-182.[doi:10.11846/j.issn.1001_8891.2019020012]
 ZHOU Chenxu,HUANG Fuzhen.Infrared and Visible Image Fusion Based on BLMD and NSDFB[J].Infrared Technology,2019,41(2):176-182.[doi:10.11846/j.issn.1001_8891.2019020012]
点击复制

基于BLMD和NSDFB算法的红外与可见光图像融合方法
分享到:

《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
41卷
期数:
2019年第2期
页码:
176-182
栏目:
出版日期:
2019-02-22

文章信息/Info

Title:
Infrared and Visible Image Fusion Based on BLMD and NSDFB
文章编号:
1001-8891(2019)02-0176-07
作者:
周晨旭黄福珍
上海电力学院 自动化工程学院,上海 200090
Author(s):
ZHOU ChenxuHUANG Fuzhen
College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
关键词:
图像融合二维局部均值分解非下采样方向滤波器组残余分量
Keywords:
image fusionBLMDNSDFBresidue
分类号:
TP391.41
DOI:
10.11846/j.issn.1001_8891.2019020012
文献标志码:
A
摘要:
针对传统图像融合方法容易导致融合图像出现细节不明显和目标信息不完整的问题,本文提出一种基于二维局部均值分解(Bidimensional Local Mean Decomposition,BLMD)和非下采样方向滤波器组(Nonsubsampled Directional Filter Banks,NSDFB)算法的红外与可见光图像融合方法(基于方向滤波的二维局部均值分解法,Bidimensional Local Mean Decomposition based Directional Filtering Analysis,BLMDDFA)。首先,计算两幅原始图片的熵值,同时提取熵值较大的图片的残余分量,该残余分量与另一张原始图片有着较强的相关性。然后,通过BLMD和NSDFB算法将残余分量和熵值较小的原始图片分解成低频子带和一系列不同尺度的高频方向子带,并使用不同的融合规则分别对低频子带和高频子带进行融合。最后,通过相应的逆变换运算获得融合图像。实验结果表明,本文方法的融合性能在对比度、细节信息展示和目标突出方面均高于经典的融合算法,在信息熵、标准差、空间频率和平均梯度方面较Laplacian方法中各指标分别提高了5.6%、28.9%、37.4%和47.6%,信噪比较Laplacian方法降低了8.5%。
Abstract:
Because traditional image fusion methods can easily cause blurred details and dim targets, a new fusion approach based on bidimensional local mean decomposition(BLMD) and nonsubsampled directional filter banks(NSDFBs) for visible–infrared images is proposed. In this fusion framework, the entropies of two source images are first calculated, and the residue of the image whose entropy is larger is extracted, which is highly relevant for the other source images. Then, the residue and the other source image are decomposed into low-frequency subbands and a sequence of high-frequency directional subbands in different scales by using BLMD and NSDFBs. At the fusion stage, two relevant fusion rules are used in low-frequency subbands and high-frequency directional subbands, respectively. Finally, the fused image is obtained by applying the corresponding inverse transform. Experimental results show that the proposed fusion algorithm can obtain state-of-the-art performance for visible–infrared fusion images in the aspects of both objective assessment and subjective visual quality, even when the source images are captured in different conditions. Furthermore, the fused results have higher contrast, richer details, and more-remarkable targets than those of Laplacian image fusion methods, increasing by 5.6%, 28.9%, 37.4%, and 47.6% in the information entropy(IE), standard deviation(SD), spatial frequency(SF) and average gradient(AG), respectively, while decreasing by 8.5% in peak signal-to-noise ratio.

参考文献/References:

[1] 韩博, 张鹏辉, 许辉, 等. 基于区域的二维经验模式分解的图像融合算法[J]. 红外技术, 2013, 35(9): 546-550.
HAN Bo, ZHANG Penghui, XU Hui, et al. Region-based image fusion algorithm using bidimensional empirical mode decomposition[J]. Infrared Technology, 2013, 35(9): 546-550.
[2] 杨风暴. 红外偏振与光强图像的拟态融合原理和模型研究[J]. 中北大学学报: 自然科学版, 2017, 38(1): 1-8.
YANG Fengbao. Research on Theory and model of mimic fusion between infrared polarization and intensity images[J]. Journal and North University of China: Natural Science Edition, 2017, 38(1): 1-8.
[3] 宋建辉, 甘晶, 刘砚菊. 基于PCNN与区域特征的红外与可见光图像融合[J]. 计算机工程与应用, 2016, 52(8): 186-190.
SONG Jianhui, GAN Jing, LIU Yanju. Infrared and visible image fusion based on PCNN and region characters[J]. Computer Engineering and Applications, 2016, 52(8): 186-190.
[4] 杨桄, 童涛, 孟强强. 基于梯度加权的红外与可见光图像融合方法[J]. 红外与激光工程, 2014, 43(8): 2772-2779.
YANG Guang, TONG Tao, MENG Qiangqiang. Infrared and visible images fusion method based on gradient weighted[J]. Infrared and Laser Engineering, 2014, 43(8): 2772-2779.
[5] 江铁成. 一种改进PCA与IHS融合的高光谱图像异常检测算法[J]. 计算机工程与科学, 2016, 38(4): 733-738.
JIANG Tiecheng. An improved anomaly detection algorithm for hyperspectral images based on PCA and HIS fusion[J]. Computer Engineering and Science, 2016, 38(4): 733-738.
[6] 彭延军, 王瑾瑾, 王元红. 基于拉普拉斯金字塔改进的图像融合方法[J]. 软件导刊, 2016, 15(1): 167-170.
PENG Yanjun, WANG Jinjin, WANG Yuanhong. Infrared and visible images fusion method based on improved Laplacian pyramid[J]. Software Guide, 2016, 15(1): 167-170.
[7] LIU X, MEI W, DU H, et al. A novel image fusion algorithm based on nonsubsampled shearlet transform and morphological component analysis [J]. Signal Image & Video Processing, 2016, 10(5): 959-966.
[8] ZHANG L, LUO C G, ZHANG Y Y, et al. Fusion algorithm of infrared and visible images based on support value transform[J]. Laser Technology, 2015, 39(3): 428-431.
[9] Kaur H, Rani J. Image fusion on digital images using Laplacian pyramid with DWT[C]//Third International Conference on Image Information Processing of IEEE, 2016: 393-398.
[10] 金炜, 励金祥, 周亚训. 抗混叠轮廓波变换及其在图像融合中的应用[J]. 红外与毫米波学报, 2009, 28(5): 392-395.
JIN Wei, LI Jinxiang, ZHOU Yaxun. Aliasing-free contourlet transform and it’s application in image fusion[J]. Journal of Infrared and Millimeter Waves, 2009, 28(5): 392-395.
[11] LI S, YANG B, HU J. Performance comparison of different multi -resolution transforms for image fusion[J]. Information Fusion, 2012, 12(2): 74-84.
[12] 郭明, 符拯, 奚晓梁. 基于局部能量的NSCT域红外与可见图像融合算法[J]. 红外与激光工程, 2012, 41(8): 2229-2235.
GUO Ming, FU Zheng, XI Xiaoliang. Novel fusion algorithm for infrared and visible images based on local energy in NSCT domain[J]. Infrared and Laser Engineering, 2012, 41(8): 2229-2235.
[13] Jonathan S Smith. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface, 2005, 2(5): 443 - 454.
[14] 陈思汉, 余建波. 基于二维局部均值分解的图像多尺度分析处理[J]. 计算机辅助设计与图形学学报, 2015, 27(10): 1842-1850.
CHEN Sihan, YU Jianbo. Multiscale image analysis based on bidimensional local mean decomposition[J]. Journal of Computer-Aided Design & Computer Graphics, 2015, 27(10): 1842-1850.
[15] Shanthi S A, Sulochana C H, Jerome S A. Image denoising using bilateral filter in subsampled pyramid and nonsubsampled directional filter bank domain[J]. Journal of Intelligent & Fuzzy Systems, 2016, 31(1): 237-247.
[16] Indira K P, Hemamalini R R, Nandhitha N M. Performance evaluation of DWT, SWT and NSCT for fusion of PET and CT Images using different fusion rules[J]. Biomedical Research, 2016, 27(1): 123-131.
[17] CHEN Y, XIONG J, LIU H L, et al. Fusion method of infrared and visible images based on neighborhood characteristic and regionalization in NSCT domain[J]. Optik-International Journal for Light and Electron Optics, 2014, 125(17): 4980-4984.

相似文献/References:

[1]周萧,杨风暴,蔺素珍,等. 基于自适应滑动窗口的双色中波红外图像融合方法研究[J].红外技术,2013,35(04):227.
 ZHOU Xiao,YANG Feng-bao,LIN Su-zhen,et al.The Study on Fusion Method of Dual-color MWIR Images Based on Adaptive Sliding Window[J].Infrared Technology,2013,35(2):227.
[2]安富,杨风暴,蔺素珍,等. 基于局部能量与模糊逻辑的红外偏振图像融合[J].红外技术,2012,34(10):573.
 AN Fu,YANG Feng-bao,LIN Su-zhen,et al.Infrared Polarization Images Fusion Based on Local Energy and Fuzzy Logic[J].Infrared Technology,2012,34(2):573.
[3]杨 锋,张俊举,许 辉,等.一种图像融合算法硬件实现[J].红外技术,2013,35(09):541.[doi:10.11846/j.issn.1001_8891.201309003]
 YANG Feng,ZHANG Jun-ju,XU Hui,et al.Hardware Implementation of an Image Fusion Method[J].Infrared Technology,2013,35(2):541.[doi:10.11846/j.issn.1001_8891.201309003]
[4]韩 博,张鹏辉,许 辉,等.基于区域的二维经验模式分解的图像融合算法[J].红外技术,2013,35(09):546.[doi:10.11846/j.issn.1001_8891.201309004]
 HAN Bo,ZHANG Peng-hui,XU Hui,et al.Region-based image fusion algorithm using bidimensional empirical mode decomposition[J].Infrared Technology,2013,35(2):546.[doi:10.11846/j.issn.1001_8891.201309004]
[5]何永强,王群,王国培,等.基于融合和色彩传递的灰度图像彩色化技术[J].红外技术,2012,34(05):276.
 HE Yong-qiang,WANG Qun,WANG Guo-pe,et al.Gray Image Colorization Based on Fusion and Color Transfer[J].Infrared Technology,2012,34(2):276.
[6]李伟伟,杨风暴,蔺素珍,等.红外偏振与红外光强图像的伪彩色融合研究[J].红外技术,2012,34(02):109.
 LI Wei-wei,YANG Feng-bao,LIN Su-zhen,et al.Study on Pseudo-color Fusion of Infrared Polarization and Intensity Image[J].Infrared Technology,2012,34(2):109.
[7]徐中中,曲仕茹.新型可见光和红外图像融合综合评价方法[J].红外技术,2011,33(10):568.
 XU Zhong-zhong,QU Shi-ru.A New Comprehensive Evaluation of Visible and Infrared Image Fusion[J].Infrared Technology,2011,33(2):568.
[8]薛模根,刘存超,徐国明,等.基于多尺度字典的红外与微光图像融合[J].红外技术,2013,35(11):696.[doi:10.11846/j.issn.1001_8891.201311005]
 XUE Mo-gen,LIU Cun-chao,XU Guo-ming,et al.Infrared and Low Light Level Image Fusion Based on Multi-scale Dictionary[J].Infrared Technology,2013,35(2):696.[doi:10.11846/j.issn.1001_8891.201311005]
[9]孙爱平,龚杨云,朱尤攀,等.微光与红外图像融合手持观察镜光学系统设计[J].红外技术,2013,35(11):712.[doi:10.11846/j.issn.1001_8891.201311008]
 SUN Ai-ping,GONG Yang-yun,ZHU You-pan,et al.Optical System Design of Low-light-level and Infrared Image Fusion Hand-held Viewer[J].Infrared Technology,2013,35(2):712.[doi:10.11846/j.issn.1001_8891.201311008]
[10]何永强,周云川,仝红玉,等.基于YCBCR空间颜色传递的融合图像目标检测算法[J].红外技术,2011,33(06):349.
 HE Yong-qiang,ZHOU Yun-chuan,TONG Hong-yu,et al.A Target Detection Method Based on Color Transferin YCBCR Space for Fused Image[J].Infrared Technology,2011,33(2):349.

备注/Memo

备注/Memo:
收稿日期:2017-08-21;修订日期:2017-10-10.
作者简介:周晨旭(1992-),男,浙江杭州人,硕士研究生,研究方向红外图像信息处理。E-mail:zhouchenxush@163.com。
通信作者:黄福珍(1976-),女,副教授,博士,主要研究方向为人脸信息处理和红外图像处理。E-mail:huangfzh@shiep.edu.cn。
基金项目:上海市电站自动化技术重点实验室资助项目(13DZ2273800)。
更新日期/Last Update: 2019-02-21