Fusion of infrared and visible images based on target segmentation and compressed sensing

被引：4
|
作者：
Wang X. [1 ,2 ]
Ji T.-B. [3 ]
Liu F. [2 ]
机构：
[1] School of Computer Science & Engineering, Changchun University of Technology, Changchun
[2] College of Communication Engineering, Jilin University, Changchun
[3] Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun
来源：
Liu, Fu (liufu@jlu.edu.cn) | 1743年 / Chinese Academy of Sciences卷 / 24期
关键词：
Compressed sensing; Image fusion; Infrared image; Multi-resolution singular value decomposition; Non-subsampled shearlet transform; Saliency map; Target segmentation; Visible image;
D O I：
10.3788/OPE.20162407.1743
中图分类号：
学科分类号：
摘要：
The image fusion of infrared and visible light is susceptible to noise and the target information is weakened easily. Therefore, a new fusion algorithm based on target area extraction and compressed sensing was proposed. Firstly, the infrared image was detected in a salient region at frequency-tuned domain to obtain a corresponding salient map. Under the guidance of the salient map, the infrared target area was extracted together with region growing method to effectively overcome the effects of noise and complex background interference on target segmentation. Then, non-subsampled shearlet transform was adopted to decompose the source images and the high and low frequency sub bands in the target and backgound regions were fused respectively. Finally, a new fusion rule was proposed based on multi-resolution singular value decomposition and compressed sensing, and the fused image was reconstructed by the non-subsampled shearlet inverse transform. As compared with the other algorithms, experimental results show that the algorithm highlights the target area, preserves the details of the source images and suppresses the noise interference. The image fusion quality evaluation indexes including information entropy, standard deviation, mutual information and transferred edge information have increased by 3.94%, 19.14%, 9.96% and 8.52%, respectively. © 2016, Science Press. All right reserved.
引用
收藏
页码：1743 / 1753
页数：10
相关论文
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