Infrared and Laser Engineering, Volume. 51, Issue 3, 20210139(2022)

Infrared and visible image fusion of convolutional neural network and NSST

Kewei Huan, Xiangyang Li, Yutong Cao, and Xiao Chen
Author Affiliations
  • College of Physics, Changchun University of Science and Technology, Changchun 130022, China
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    Figures & Tables(10)
    Convolutional neural network structure
    Cross entropy loss function
    Infrared image ''UN Camp''and images after saliency extraction by various methods. (a) Infrared image "UN Camp"; (b) Standard segmentation of image "UN Camp"; (c) AC method; (d) SR method; (e) LC method; (f) FT method; (g) CNN method; (h) CNN+FT method
    Infrared image ''dune'' and images after saliency extraction by various methods. (a) Infrared image "dune"; (b) Standard segmentation of image "dune"; (c) AC method; (d) SR method; (e) LC method; (f) FT method; (g) CNN method; (h) CNN+FT method
    Image fusion model based on convolutional neural network and NSST
    ''UN Camp'' infrared and visible images and fusion results. (a) Infrared image; (b) Visible image; (c) DWT method; (d) CS method; (e) BEMD method; (f) NSCT+FL method; (g) NSST+FL method; (h) Proposed method; (i) Significant area fusion image
    ''dune'' infrared and visible images and fusion results. (a) Infrared image; (b) Visible image; (c) DWT method; (d) CS method; (e) BEMD method; (f) NSCT+FL method; (g) NSST+FL method; (h) Proposed method; (i) Significant area fusion image
    ''iron'' infrared and visible images and fusion results. (a) Infrared image; (b)Visible image; (c) DWT method; (d) CS method; (e) BEMD method; (f) NSCT+FL method; (g) NSST+FL method; (h) Proposed method; (i) Significant area fusion image
    • Table 1. Target significance extraction evaluation index MAE

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      Table 1. Target significance extraction evaluation index MAE

      MethodACSRLCFTCNNCNN+FT
      MAE115.641.008.599.340.500.27
      MAE28.450.552.912.910.630.35
    • Table 2. Infrared and visible image fusion effect evaluation

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      Table 2. Infrared and visible image fusion effect evaluation

      ImageMethodEAGSFMICE
      UNDWT6.934 27.049 213.921 72.668 30.356 3
      CampCS6.252 74.965 910.300 51.593 30.599 9
      BEMD6.603 86.146 212.007 71.573 80.575 9
      NSCT+FL6.810 57.110 914.126 52.432 80.326 4
      NSST+FL6.855 57.051 614.475 82.355 00.279 8
      Proposed method7.116 38.008 216.106 92.991 90.264 8
      DuneDWT6.657 46.507 512.371 82.594 30.307 2
      CS5.903 84.694 89.790 91.188 40.654 5
      BEMD6.156 65.426 710.391 41.196 50.576 0
      NSCT+FL6.675 86.578 315.540 92.222 00.292 5
      NSST+FL6.666 77.365 313.954 22.637 00.301 4
      Proposed method6.701 17.386 814.153 92.815 70.289 2
      IronDWT6.677 812.647 633.730 43.617 30.550 3
      CS6.537 27.647 520.025 43.177 30.498 7
      BEMD6.677 79.090 623.349 13.392 50.539 7
      NSCT+FL6.767 714.998 138.969 73.397 50.474 5
      NSST+FL6.751 115.645 440.410 43.177 40.427 9
      Proposed method6.768 716.282 241.718 83.701 30.409 3
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    Kewei Huan, Xiangyang Li, Yutong Cao, Xiao Chen. Infrared and visible image fusion of convolutional neural network and NSST[J]. Infrared and Laser Engineering, 2022, 51(3): 20210139

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    Paper Information

    Category: Image processing

    Received: Nov. 22, 2021

    Accepted: --

    Published Online: Apr. 8, 2022

    The Author Email:

    DOI:10.3788/IRLA20210139

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