• Acta Photonica Sinica
  • Vol. 51, Issue 3, 0310002 (2022)
Jiping SUN and Weiqiang FAN*
Author Affiliations
  • School of Mechanical Electronic and Information Engineering,China University of Mining and Technology (Beijing),Beijing 100083,China
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    DOI: 10.3788/gzxb20225103.0310002 Cite this Article
    Jiping SUN, Weiqiang FAN. Mine Dual-band Image Fusion in MS-ADoG Domain Combined with ReNLU and VGG-16[J]. Acta Photonica Sinica, 2022, 51(3): 0310002 Copy Citation Text show less
    Framework of FIR and VIS image fusion based on Laplace of Gaussian transform
    Fig. 1. Framework of FIR and VIS image fusion based on Laplace of Gaussian transform
    Fusion framework of FIR and VIS image based on VGG-16
    Fig. 2. Fusion framework of FIR and VIS image based on VGG-16
    VIS image,basic image and detailed image
    Fig. 3. VIS image,basic image and detailed image
    The functional relationship between the weight w and the critical value T
    Fig. 4. The functional relationship between the weight w and the critical value T
    The fusion strategy framework of detailed images
    Fig. 5. The fusion strategy framework of detailed images
    Framework diagram of fusion algorithm
    Fig. 6. Framework diagram of fusion algorithm
    FIR and VIS images and fusion images
    Fig. 7. FIR and VIS images and fusion images
    Line chart of evaluation indicators
    Fig. 8. Line chart of evaluation indicators
    ImagesIndicatorsAlgorithms
    SVM⁃WLSLatLRRImproved IHS Curvetlet

    DLF⁃

    VGG16

    Resnet50⁃

    ZCA

    Proposed Method
    EFMMFAG0.023 00.008 30.005 90.006 60.005 50.008 0
    RMSE0.175 40.161 80.319 30.160 90.160 20.222 2
    HE0.490 70.497 40.502 70.499 80.499 60.504 5
    CC9.735 99.655 68.787 310.167 010.303 08.467 1
    DI1.424 00.451 41.502 30.552 40.560 01.148 3
    ART0.910 125.795 10.859 82.855 61.797 61.692 4
    UECAG0.039 70.013 30.008 50.013 20.010 40.0108
    RMSE0.205 30.195 00.278 10.194 50.192 50.249 2
    HE0.490 40.496 10.503 70.499 60.499 60.507 0
    CC11.247 711.337 810.576 012.534 312.290 29.943 5
    DI1.339 40.590 71.562 80.705 60.725 41.720 5
    ART0.460 723.764 70.228 32.339 71.352 41.671 8
    4⁃3 CATRAG0.016 10.004 80.003 90.004 20.003 40.006 0
    RMSE0.233 00.214 20.309 80.208 90.209 10.290 3
    HE0.485 00.495 80.506 00.499 70.499 60.512 8
    CC10.970 110.890 99.789 111.796 111.770 48.982 5
    DI1.398 90.532 12.801 50.606 60.602 20.832 0
    ART0.448 725.951 10.205 72.410 21.235 11.708 6
    FMMFAG0.056 10.019 10.013 60.017 90.013 60.015 3
    RMSE0.190 30.168 10.259 90.161 90.162 10.207 2
    HE0.494 90.497 40.502 50.499 70.499 80.503 4
    CC10.557 610.144 510.649 411.283 011.244 310.267 3
    DI1.612 00.370 20.503 30.418 60.427 10.834 6
    ART0.452 225.113 80.225 32.996 91.285 71.660 4
    Table 1. Performance indicators of different algorithms under 4 types of source images
    Fusion algorithmsPerformance evaluation indicators
    AGRMSEHECCDIART
    SVM⁃WLS0.032 70.198 70.493 010.690 91.610 00.507 0
    LatLRR0.011 50.185 30.497 310.686 70.520 024.502 4
    Improved IHS Curvetlet0.007 40.271 10.503 510.290 91.585 70.288 8
    DLF⁃VGG160.010 40.182 40.499 711.654 10.644 22.463 5
    Resnet50⁃ZCA0.008 20.182 20.499 711.572 60.619 31.334 5
    Proposed method0.010 30.242 30.506 19.753 21.698 81.689 0
    Table 2. Average performance indicators of different fusion methods
    Jiping SUN, Weiqiang FAN. Mine Dual-band Image Fusion in MS-ADoG Domain Combined with ReNLU and VGG-16[J]. Acta Photonica Sinica, 2022, 51(3): 0310002
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