Optics and Precision Engineering, Volume. 31, Issue 23, 3490(2023)

Infrared and visible image fusion based on target enhancement and butterfly optimization

Shuai HAO... Tong LI, Xu MA*, Tian HE, Xizi SUN and Jiahao LI |Show fewer author(s)
Author Affiliations
  • College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an710054, China
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    Figures & Tables(11)
    Flowchart of TEBOFuse method
    Experimental results of visible light image enhancement
    Experimental results of infrared image enhancement
    Fusion results
    Subjective comparison results of ablation experiments
    Subjective comparison of experimental results
    • Table 1. [in Chinese]

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      Table 1. [in Chinese]

      Input:IVIS and IIR.
      Output:F

      Step 1: 基于MSRCR的可见光增强

      forIVIS

      do IVISS=MSRCR(IVIS)

      Step 2: 基于TEE的红外图像增强

      forIIR

      do IIRb=BF(IIR)

      ThenIIRc=IIR-IIRb

      IIRp=HGM(IIR)

      IIRe=canny(IIR)

      ThenIIRS=IIRc+IIRp+IIRe

      Step 3: 基于FPDE-PCA的伪影去除.

      Step 3.1. FPDE图像分解.

      forIVISSIIRS

      doIIR,VISB=FPDE(IIR,VISS)IIR,VISD=IIR,VISS-IIR,VISB

      Step 3.2. 基于PCA的细节层融合.

      do ID=P1IVISD+P2IIRD

      Step 3.3. 基于区域特性量测的基础层融合.

      if M(i,j)<T

      then IB(i,j)=LPl(i,j)

      if M(i,j)T

      then IB(i,j)=ω(i,j)LPl(i,j)

      Step 4: 基于BOA的图像重建

      for x,minQSF+QAG+QPMI_pixel

      do xit+1=xit+(r2×x-xit)×fi

      thenF=xID+IB

    • Table 1. Experimental platform

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      Table 1. Experimental platform

      名称配 置
      CPUIntel(R)Core(TM)i5-8250u
      内存8G
      操作系统Windows 11
      软件MATLAB R2016b
    • Table 2. Objective comparison of ablation experimental results

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      Table 2. Objective comparison of ablation experimental results

      实验

      方案

      基于MSRCR的可见光增强模块基于TEE的红外图像增强模块基于FPDE-PCA的伪影去除模块基于蝴蝶优化的图像重建模块ENSFSDJEVIFNSS
      实验一6.921 711.418 936.329 310.975 70.839 92.940 7
      实验二6.663 611.582 730.260 510.959 40.694 42.729 1
      实验三6.695 27.098 528.863 611.124 90.741 63.051 3
      实验四6.905 210.957 835.255 111.767 50.871 92.997 1
      TEBOFuse7.287 312.398 149.066 412.719 81.080 13.200 7
    • Table 3. Objective comparison results of optimization algorithms

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      Table 3. Objective comparison results of optimization algorithms

      算法ENSFSDJEVIFNSS
      DE5.987 18.791 821.285 710.015 40.560 12.812 9
      FA6.179 910.337 330.856 111.946 20.953 72.729 1
      GA6.539 410.440 930.384 311.789 60.678 43.051 3
      PSO6.183 610.178 742.643 211.376 70.823 13.213 2
      BOA7.272 111.881 348.701 912.722 81.092 33.188 5
    • Table 4. Objective evaluation indicators of different fusion methods

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      Table 4. Objective evaluation indicators of different fusion methods

      融合算法ENSFSDJEVIFNSS
      HMSD6.592 811.652 129.189 412.033 70.811 02.557 1
      CNN6.923 411.092 338.768 512.115 90.940 22.931 6
      IFCNN6.589 211.889 331.497 712.019 00.730 02.606 6
      RFN-Nest6.869 46.508 636.076 912.255 70.854 32.816 3
      DenseFuse6.193 76.324 722.791 711.711 60.613 72.405 0
      NestFuse6.919 89.973 839.917 111.824 60.946 12.850 2
      TEBOFuse7.287 312.398 149.066 412.719 81.080 13.200 7
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    Shuai HAO, Tong LI, Xu MA, Tian HE, Xizi SUN, Jiahao LI. Infrared and visible image fusion based on target enhancement and butterfly optimization[J]. Optics and Precision Engineering, 2023, 31(23): 3490

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    Paper Information

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    Received: May. 13, 2023

    Accepted: --

    Published Online: Jan. 5, 2024

    The Author Email: MA Xu (haoxust@163.com)

    DOI:10.37188/OPE.20233123.3490

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