Infrared Polarization and Intensity Image Fusion Based on Dual-Tree Complex Wavelet Transform and Sparse Representation

被引：14
|
作者：
Zhu P. [1 ]
Liu Z.-Y. [1 ]
Huang Z.-H. [1 ]
机构：
[1] Key Laboratory of Opto-electronic Information Technology, Ministry of Education, Tianjin University, Tianjin
来源：
Huang, Zhan-Hua (zhanhua@tju.edu.cn) | 1600年 / Chinese Optical Society卷 / 46期
基金：
中国国家自然科学基金;
关键词：
Dual-Tree Complex Wavelet Transform; Image fusion; Infrared polarization image; K-singular value decomposition; Sparse representation;
D O I：
10.3788/gzxb20174612.1210002
中图分类号：
学科分类号：
摘要：
Considering that infrared polarization and intensity image contain common information and their own unique information, a method of image fusion based on Dual-Tree Complex Wavelet Transform and sparse representation was proposed. Firstly, the high and low frequency components of source images wereobtained by using Dual-Tree Complex Wavelet Transform, and the high frequency components werecombined by absolute maximum method. Secondly, a joint matrix was constructed by low frequency components, and a redundant dictionarywas acquired by using K-singular value decomposition to train the matrix. Based on the dictionary, the sparse coefficient of low frequency component was calculated, and the common information and unique information werejudged by the location of non-zero value of the sparse coefficient, and two kinds of information was merged by proper fusion rules. Finally, the fusion image was obtained by performing inverse Dual-Tree Complex Wavelet Transform on the fused high and low frequency components. The experimental results show that the proposed fusion method can highlight the common information of source images and keep their own unique information, and the fusion image own higher contrast and clearer details. © 2017, Science Press. All right reserved.
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