• Laser & Optoelectronics Progress
  • Vol. 60, Issue 2, 0210006 (2023)
Jiaming Liang, Shen Yang*, and Lifan Tian
Author Affiliations
  • School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China
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    DOI: 10.3788/LOP212636 Cite this Article Set citation alerts
    Jiaming Liang, Shen Yang, Lifan Tian. Infrared and Visible Image Fusion Based on Image Enhancement and Rolling Guidance Filtering[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0210006 Copy Citation Text show less
    Framework of the proposed algorithm
    Fig. 1. Framework of the proposed algorithm
    Enhancement results for visible images. (a) Visible images; (b) enhancement results obtained by CE algorithm; (c) enhancement results obtained by proposed algorithm
    Fig. 2. Enhancement results for visible images. (a) Visible images; (b) enhancement results obtained by CE algorithm; (c) enhancement results obtained by proposed algorithm
    Brightness layer extraction results. (a) Original images; (b) results of brightness layer extracted by ME-AV-G; (c) results of brightness layer extracted by proposed method
    Fig. 3. Brightness layer extraction results. (a) Original images; (b) results of brightness layer extracted by ME-AV-G; (c) results of brightness layer extracted by proposed method
    Block diagram of multi-scale decomposition
    Fig. 4. Block diagram of multi-scale decomposition
    Effectiveness analysis of image enhancement module. (a) Fusion results without image enhancement module; (b) fusion results using image enhancement module
    Fig. 5. Effectiveness analysis of image enhancement module. (a) Fusion results without image enhancement module; (b) fusion results using image enhancement module
    Fusion results under different decomposition stages. (a) 3-grade decomposition fusion result; (b) 4-grade decomposition fusion result; (c) 5-grade decomposition fusion result; (d) 6-grade decomposition fusion result
    Fig. 6. Fusion results under different decomposition stages. (a) 3-grade decomposition fusion result; (b) 4-grade decomposition fusion result; (c) 5-grade decomposition fusion result; (d) 6-grade decomposition fusion result
    Fusion results obtained by different fusion methods for road image
    Fig. 7. Fusion results obtained by different fusion methods for road image
    Fusion results obtained by different fusion methods for tree image
    Fig. 8. Fusion results obtained by different fusion methods for tree image
    Fusion results obtained by different fusion methods for school image
    Fig. 9. Fusion results obtained by different fusion methods for school image
    Fusion results obtained by different fusion methods for camp image
    Fig. 10. Fusion results obtained by different fusion methods for camp image
    AG line chart
    Fig. 11. AG line chart
    IE line chart
    Fig. 12. IE line chart
    EI line chart
    Fig. 13. EI line chart
    Qcb line chart
    Fig. 14. Qcb line chart
    SF line chart
    Fig. 15. SF line chart
    Decomposition layerSFAGEIQcbIE
    313.39535.479453.24640.49717.1966
    413.57665.559254.16710.49527.2032
    513.62835.624654.92540.49227.2131
    613.74685.686355.62000.48517.2263
    Table 1. Objective indicator analysis of the fusion of different decomposition series
    Sourse imgaeMethodSFAGEIQcbIE
    RoadHMSD17.56746.007963.11500.55156.7081
    LATLRR12.18484.081143.29280.54296.3014
    LP16.32905.494158.07960.50806.6372
    IFEVIP14.11625.410657.85830.54746.7129
    NSCT_SR16.70095.857062.41930.60406.8281
    VSMWLS16.03355.183153.80140.55576.2132
    MGFF14.86785.464457.86540.50616.6685
    MDLATLRR16.67945.628959.80570.49756.5621
    CNN16.21215.501358.12580.59726.7048
    RFN-NEST6.46142.290425.00550.51265.8536
    Proposed method23.96378.163982.52670.57346.6276
    TreeHMSD23.85109.229894.83320.65696.7835
    LATLRR22.36508.857091.78810.68326.8747
    LP21.76028.303585.29990.62276.5519
    IFEVIP24.67689.8544101.80780.69497.0194
    NSCT_SR21.24178.175183.97550.69286.7355
    VSMWLS21.79118.311084.52470.67936.6296
    MGFF26.934910.1338104.99810.55876.1870
    MDLATLRR23.90899.183795.20200.61816.4839
    CNN21.38908.181383.96450.6436.8264
    RFN-NEST10.84564.690050.71990.61346.4651
    Proposed method41.155718.2584183.01800.63037.5858
    SchoolHMSD10.64504.328242.13930.46727.1635
    LATLRR6.33932.515125.03820.49296.7015
    LP9.23273.821137.62720.48876.7776
    IFEVIP9.09313.710236.73820.47216.9825
    NSCT_SR9.71354.104641.38970.49887.0301
    VSMWLS10.37174.244540.93540.46576.9551
    MGFF10.00964.220241.72720.50016.8664
    MDLATLRR9.00273.640236.25170.46706.7518
    CNN9.26413.818137.64190.49967.1048
    RFN-NEST4.63681.838119.83480.49447.0005
    Proposed method13.57665.559254.16710.49527.2032
    CampHMSD12.95885.373354.90350.57366.9385
    LATLRR8.41603.649438.17720.52906.5826
    LP10.96814.633847.90020.57306.6718
    IFEVIP10.40974.528046.59330.48376.7702
    NSCT_SR10.76144.643748.80540.55876.8129
    VSMWLS11.77734.921149.74690.59466.6122
    MGFF11.85345.178853.64990.56406.8117
    MDLATLRR10.15954.314845.02090.56946.5432
    CNN10.95954.655948.14310.57777.0665
    RFN-NEST7.31923.469537.72550.57756.8510
    Proposed method16.56197.357575.40300.54617.0468
    Table 2. Objective evaluation indicators of different fusion methods
    Jiaming Liang, Shen Yang, Lifan Tian. Infrared and Visible Image Fusion Based on Image Enhancement and Rolling Guidance Filtering[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0210006
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