期刊
OPTICS COMMUNICATIONS
卷 335, 期 -, 页码 168-177出版社
ELSEVIER
DOI: 10.1016/j.optcom.2014.07.093
关键词
Compressive sensing; Image fusion; DWT; CoSaMP; Gaussian matrix
类别
资金
- National Natural Science Foundation of China [61374135, 61203321, 61472053, 61173129]
- China Postdoctoral Science Foundation [2012M521676]
- Postdoctoral Scientific Research Project of Chongqing special funding [XM201307]
- Specialized Research Fund for the Doctoral Program of Higher Education of China [20120191110026]
- China Central Universities Foundation [106112013 CDJZR170005]
An appropriate fusion of infrared and visible images can integrate their complementary information and obtain more reliable and better description of the environmental conditions. Compressed sensing theory, as a low signal sampling and compression method based on the sparsity of signal under a certain transformation, is widely used in various fields. Applying to the image fusion applications, only a part of sparse coefficients are needed to be fused. Furthermore, the fused sparse coefficients can be used to accurately reconstruct the fused image. The CS-based fusion approach can greatly reduce the computational complexity and simultaneously enhance the quality of the fused image. In this paper, an improved image fusion scheme based on compressive sensing is presented. This proposed approach can preserve more detail information, such as edges, lines and contours in comparison to the conventional transform-based image fusion approaches. In the proposed approach, the sparse coefficients of the source images are obtained by discrete wavelet transform. The low and high coefficients of infrared and visible images are fused by an improved entropy weighted fusion rule and a max-abs-based fusion rule, respectively. The fused image is reconstructed by a compressive sampling matched pursuit algorithm after local linear projection using a random Gaussian matrix. Several comparative experiments are conducted. The experimental results show that the proposed image fusion scheme can achieve better image fusion quality than the existing state-of-the-art methods. (C) 2014 Elsevier B.V. All rights reserved.
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