基于非下采样Contourlet的熵测度图像融合算法

兵工学报 ›› 2011, Vol. 32 ›› Issue (11): 1348-1352.

基于非下采样Contourlet的熵测度图像融合算法

李骜¹，李一兵¹，刘丹丹²

(1.哈尔滨工程大学信息与通信工程学院, 黑龙江哈尔滨 150001;2.黑龙江科技学院现代制造加工中心黑龙江哈尔滨 150001）

收稿日期:2010-11-01 修回日期:2010-11-01 上线日期:2014-05-04
通讯作者: 李骜 E-mail:dargonboy@126.com
作者简介:李骜（1986—），男，博士研究生
基金资助:
国家自然科学基金资助项目（50904025）；船舶工业国防科技预研项目（10J3.16）

An Image Fusion Algorithm with Entropy Measure Based on Non-subsampled Contourlet

LI Ao¹, LI Yi-bing¹, LIU Dan-dan²

(1.College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang,China；2.Modem Manufacturing Engineering Center, Heilongjiang Institute of Science and Technology,Harbin 150001, Heilongjiang, China)

Received:2010-11-01 Revised:2010-11-01 Online:2014-05-04
Contact: LI Ao E-mail:dargonboy@126.com

摘要/Abstract

摘要： 活性测度是图像融合过程中的重要衡量工具。通过其提取的某种特征信息，来决定哪幅输入图像的特征更为明显。在基于多分辨率分解的融合算法中，常用的活性测度只考虑各层高频子带系数本身，忽略了低频系数所提供的信息。考虑到综合高、低频系数对于活性测度的影响，基于非下采样Contourlet变换良好的平移不变性、多方向等特性，提出了一种在一般方法的基础上，添加由低频系数所得的掩模，再作为活性测度的融合算法。给出了不同活性测度的融合图像以及客观性能评价指标。结果表明，熵掩模测度的融合结果优于其他几种传统测度。

关键词: 信息处理技术, 熵, 活性测度, 图像融合, 非下采样Contourlet, 多分辨率分析

Abstract: The feature information extracted by using activity measure, a very important measure in the process of image fusion, determine a certain input image with more obvious characteristics. In wavelet-based fusion algorithm, the common activity measures consider the coefficients of all high-frequency sub-bands themselves only, and ignore the information provided by low-frequency coefficients. An algorithm based on the general method and added the entropy-masking obtained from the low-frequency coefficients as an active measure is proposed in this paper to comprehensively consider the impact of the high and low frequency coefficients on the activity measures. And, the fused image with different activity measures and objective performance evaluation index are provided also. The results show that the entropy-masking measure is better than several other traditional measures for image fusion.

Key words: information processing, entropy, activity measure, image fusion, non-subsampled Contourlet, analysis of multi-resolution

中图分类号:

TP391.9

李骜，李一兵，刘丹丹. 基于非下采样Contourlet的熵测度图像融合算法[J]. 兵工学报, 2011, 32(11): 1348-1352.

LI Ao, LI Yi-bing, LIU Dan-dan. An Image Fusion Algorithm with Entropy Measure Based on Non-subsampled Contourlet[J]. Acta Armamentarii, 2011, 32(11): 1348-1352.

参考文献

［1］储彬彬，庞璐璐，漆德宁，等. 基于多尺度分析的图像融合技术综述[J]. 航空电子技术, 2009, 40(3):29-33.
CHU Bin-bin, PANG Lu-lu, QI De-ning, et al. Summary of multi-resolution analysis based image fusion technology[J]. Avionics Technology, 2009, 40(3):29-33. (in Chinese)
[2] Huang Zhi, Yu Xianchuan, Wang Guian, et al. Application of several non-negative matrix factorization-based methods in remote sensing image fusion[C]∥Fifth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2008,4: 29-33.
[3] 赵诚，付梦印，王立平. 基于粒子群优化的多分辨率图像融合[J]. 兵工学报, 2010,31(2):171-176.
ZHAO Cheng, FU Meng-yin, WANG Li-ping. Multi- resolution image fusion using particle swarm optimization [J]. Acta Armamentarii, 2010,31(2):171-176. (in Chinese)
[4] Do M N,Vetterli M. The Contourlet transform: an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(2):2091-2106.
[5] 尚政国，赵春晖，吴宇平.非亚采样Contourlet遥感图像融合[J]. 光学工程, 2008,35(7):79-82.
SHANG Zheng-guo, ZHAO Chun-hui, WU Yu-ping. Remote sensing image fusion method based on nonsubsampled Contourlet[J]. Opto-Electronic Engineering, 2008,35(7): 79-82. (in Chinese)
[6] 田思，张俊举，袁轶慧，等. 图像融合的开窗处理技术研究[J]. 兵工学报, 2009,30(4):438-441.
TIAN Si, ZHANG Jun-ju, YUAN Yi-hui, et al. Research on image fusion technology based on windowing[J]. Acta Armamentarii, 2009,30(4):438-441. (in Chinese)
[7] Cunha A L, Zhou J, Do M N. The nonsubsample Contourlet theory, design and application[J]. IEEE Transaction on Image Processing, 2006, 15(10)：1780-1792.
[8] Mumtaz , A Majid A, Mumtaz. A Genetic algorithm and its application to image fusion[C]∥4th International Conference on Emerging Technologies. IEEE，2008：6-10.
[9] 李军，林宗坚. 基于特征的遥感影像数据融合方法[J]. 中国图象图形学报, 1997,2(2,3)：103-107.
LI Jun, LIN Zong-jian. Remote sensing image date fusion method based on feature[J]. China Journal of Image and Graphics, 1997, 2(2,3)：103-107. (in Chinese)
[10] Wilson T A, Rogers S K, Meyers L R. Perceptual-based hyperspectral image fusion using multiresolution analysis [J]. Optical Engineering, 1995: 34(11):3154-3164.
[11] 柴勇，何友，曲长文. 基于亚像素区域加权能量特征的多尺度图像融合算法[J]. 光学学报, 2009, 29(10): 2732-2736.
CHAI Yong, HE You, QU Chang-wen. Multi-scale image fusion algorithm based on subpixel weighted regi on energy[J]. Acta Optical Sinica, 2009, 29(10): 2732-2736. (in Chinese)

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