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

Wu Yiquan Wang Zhilai

吴一全, 王志来. 基于联合稀疏表示的复Contourlet域SAR图像与红外图像融合[J]. 雷达学报, 2017, 6(4): 349-358. doi: 10.12000/JR17019
引用本文: 吴一全, 王志来. 基于联合稀疏表示的复Contourlet域SAR图像与红外图像融合[J]. 雷达学报, 2017, 6(4): 349-358. doi: 10.12000/JR17019
Wu Yiquan, Wang Zhilai. SAR and Infrared Image Fusion in Complex Contourlet Domain Based on Joint Sparse Representation[J]. Journal of Radars, 2017, 6(4): 349-358. doi: 10.12000/JR17019
Citation: Wu Yiquan, Wang Zhilai. SAR and Infrared Image Fusion in Complex Contourlet Domain Based on Joint Sparse Representation[J]. Journal of Radars, 2017, 6(4): 349-358. doi: 10.12000/JR17019

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

doi: 10.12000/JR17019
Funds: The National Natural Science Foundation of China (61573183), The Open Fund of Jiangsu Key Laboratory of Big Data Analysis Technology (KXK1403), The Open Fund of Zhejiang Province Key Laboratory for Signal Processing (ZJKL_6_SP-OP 2014-02), The Open Fund of Guangxi Key Lab of Multi-Source Information Mining and Security (MIMS14-01), The Open Fund of Key Laboratory of Geo-Spatial Information Technology (KLGSIT2015-05), The Open Fund of MLR Key Laboratory of Metallogeny and Mineral Assessment Institute of Mineral Resources (ZS1406)
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    Author Bio:

    Wu Yiquan (1963–), male, professor, Ph.D. supervisor, Ph.D. degree. He received the doctorate from Nanjing University of Aeronautics and Astronautics in 1998. He is now a professor, Ph.D. supervisor of Nanjing University of Aeronautics and Astronautics. His current research interests include remote sensing image processing and understanding, target detection and identification, visual detection and image measurement, video processing and intelligence analysis, etc. He has published more than 280 papers at home and abroad academic journals. E-mail: nuaaimage@163.com

    Wang Zhilai (1992–), male, born in Jiangxi province. He is a graduate student with department of information and communication engineering at college of electronic and information engineering in Nanjing University of Aeronautics and Astronautics. His research interest includes remote sensing image processing and machine vision, etc.E-mail: 1610156025@qq.com

    Corresponding author: Wu Yiquan   nuaaimage@163.com
  • 摘要: 针对红外图像与SAR图像的灰度差异性大、两者融合图像不太符合人类视觉认知的问题,提出了一种基于联合稀疏表示的复Contourlet域红外图像与SAR图像融合方法。首先对红外图像与SAR图像分别进行复Contourlet分解。然后利用K-奇异值分解(K-Singular Value Decomposition, K-SVD)方法获得两幅源图像低频分量的过完备字典,并根据联合稀疏表示模型生成联合字典,通过正交匹配追踪(Orthogonal Matching Pursuit, OMP)方法求出源图像低频分量在联合字典下的稀疏表示系数,接着采用选择最大化策略对两个低频分量的稀疏表示系数进行选取,随后进行稀疏表示重构获得融合的低频分量;对高频分量结合视觉敏感度系数和能量匹配度两个活跃度准则进行融合,以捕获源图像丰富的细节信息。最后经复Contourlet逆变换获得融合图像。与3种经典融合方法及近年来提出的基于非下采样Contourlet变换(Non-Subsampled Contourlet Transform, NSCT)、基于稀疏表示的融合方法相比,该方法能够有效突出源图像的显著特征,最大程度地继承源图像的信息。

     

  • Figure  1.  Schematic diagram of CCT

    Figure  2.  The procedure of the proposed image fusion method

    Figure  3.  Three groups of infrared images and SAR images

    Figure  4.  Fusion results 1 by six methods

    Figure  5.  Fusion results 2 by six methods

    Figure  6.  Fusion results 3 by six methods

    Table  1.   Quantitative evaluation of six fusion methods

    Image group Fusion method IE MI CC SF AG SD Time (s)
    Group 1 LP method 6.839 11.405 0.687 32.852 22.406 35.499 2.126
    WT method 6.724 11.119 0.869 25.667 17.947 29.863 1.827
    NSCT method 6.821 11.295 0.813 26.881 18.373 33.828 50.332
    DT-CWT method 6.759 11.236 0.719 25.691 17.881 32.614 3.765
    Method in Ref. [11] 6.887 11.411 0.788 24.312 16.955 32.022 67.782
    Proposed method 7.091 11.827 0.829 30.276 20.294 43.965 27.232
    Group 2 LP method 6.973 12.070 0.739 29.114 21.005 33.825 1.728
    WT method 7.097 12.181 0.883 25.643 19.298 35.330 0.898
    NSCT method 6.895 11.808 0.729 23.456 17.272 33.062 39.590
    DT-CWT method 6.902 11.848 0.784 22.633 16.745 32.184 3.371
    Method in Ref. [11] 7.140 12.843 0.7833 30.390 22.565 37.019 53.945
    Proposed method 7.305 12.615 0.869 28.648 20.887 44.539 22.733
    Group 3 LP method 6.633 12.141 0.834 28.629 18.687 33.825 1.351
    WT method 6.797 12.1241 0.803 25.643 19.298 35.330 0.557
    NSCT method 6.801 11.973 0.864 26.105 17.539 40.052 26.433
    DT-CWT method 6.665 11.723 0.846 23.539 16.745 32.184 1.371
    Method in Ref. [11] 6.796 12.234 0.812 25.518 17.244 37.274 37.274
    Proposed method 6.864 11.8401 0.836 28.689 18.982 40.084 18.613
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出版历程
  • 收稿日期:  2017-03-01
  • 修回日期:  2017-07-08
  • 网络出版日期:  2017-08-28

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