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      SAR and Infrared Image Fusion in Complex Contourlet Domain Based on Joint Sparse Representation

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          To investigate the problems of the large grayscale difference between infrared and Synthetic Aperture Radar (SAR) images and their fusion image not being fit for human visual perception, we propose a fusion method for SAR and infrared images in the complex contourlet domain based on joint sparse representation. First, we perform complex contourlet decomposition of the infrared and SAR images. Then, we employ the KSingular Value Decomposition (K-SVD) method to obtain an over-complete dictionary of the low-frequency components of the two source images. Using a joint sparse representation model, we then generate a joint dictionary. We obtain the sparse representation coefficients of the low-frequency components of the source images in the joint dictionary by the Orthogonal Matching Pursuit (OMP) method and select them using the selection maximization strategy. We then reconstruct these components to obtain the fused low-frequency components and fuse the high-frequency components using two criteria——the coefficient of visual sensitivity and the degree of energy matching. Finally, we obtain the fusion image by the inverse complex contourlet transform. Compared with the three classical fusion methods and recently presented fusion methods, e.g., that based on the Non-Subsampled Contourlet Transform (NSCT) and another based on sparse representation, the method we propose in this paper can effectively highlight the salient features of the two source images and inherit their information to the greatest extent.

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          Author and article information

          Journal of Radars
          Chinese Academy of Sciences
          01 August 2017
          : 6
          : 4
          : 349-358
          [1 ] ①(College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China) ②(Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China) ③(Zhejiang Province Key Laboratory for Signal Processing, Zhejiang University of Technology, Hangzhou 310023, China) ④(Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin 541004, China) ⑤(Key Laboratory of Geo-Spatial Information Technology, Ministry of Land and Resources, Chengdu University of Technology, Chengdu 610059, China) ⑥(MLR Key Laboratory of Metallogeny and Mineral Assessment Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China)
          [2 ] ①(College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Technology (General)

          Remote sensing,Electrical engineering
          Image fusion,Synthetic Aperture Radar (SAR) image,Infrared image,Complex contourlet transform,Joint sparse representation


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