Laser & Optoelectronics Progress, Volume. 58, Issue 20, 2010004(2021)

Infrared and Visible Images Fusion Algorithm Based on NSST and IFCNN

Yanchun Yang*, Xiaoyu Gao, Jianwu Dang, and Yangping Wang
Author Affiliations
  • School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China
  • show less
    Figures & Tables(7)
    Decomposition process of NSST
    Framework of IFCNN
    Demonstration of feature extraction and feature fusion. (a) Infrared images; (b) visible images; (c) fusion images
    Processing flow of proposed algorithm
    Detailed image and local energy image of Kayak. (a) Original image; (b) ordinary gradient image of Fig. 5 (a); (c) energy image
    Experimental results of different algorithms. (a) Infrared images; (b) visible images; (c) DTCWT algorithm; (d) NSST algorithm; (e) Lap_cnn algorithm; (f) CSMCA algorithm; (g) ECNN algorithm; (h) proposed algorithm
    • Table 1. Objective evaluation index of different algorithms on different images

      View table

      Table 1. Objective evaluation index of different algorithms on different images

      ImageFusion algorithmAGEIENSSIMMI
      Image 1DTCWT4.230017.85386.15860.78380.9272
      NSST4.267915.26266.01850.77800.3912
      Lap_cnn4.065123.48516.54120.78950.5694
      CSMCA3.968718.75046.32240.79421.2043
      ECNN4.576820.48576.45980.84571.1254
      Proposed algorithm5.084347.58096.64410.90521.9132
      Image 2DTCWT5.073918.91806.48200.70820.7149
      NSST5.193916.89516.43130.74330.3880
      Lap_cnn4.651520.75486.32540.72570.9657
      CSMCA4.768019.43446.51910.71060.7099
      ECNN4.742118.86756.48210.71241.3484
      Proposed algorithm3.516922.98686.56610.78861.6530
      Image 3DTCWT2.460316.32126.34300.74580.4523
      NSST2.577116.57546.29640.86550.3398
      Lap_cnn2.345820.62516.32150.86570.9548
      CSMCA2.352115.84626.38830.84820.4407
      ECNN2.185419.35786.25840.85460.7984
      Proposed algorithm2.698624.67036.57310.83441.1105
      ImageFusion algorithmAGEIENSSIMMI
      Image 4DTCWT4.576818.52336.70540.79710.5582
      NSST5.149119.03486.68220.75980.3389
      Lap_cnn4.365420.35486.89780.76891.1235
      CSMCA4.031817.75416.68620.68790.5963
      ECNN2.324821.38656.94580.72480.8654
      Proposed algorithm4.159530.73777.06770.79741.3358
      Image 5DTCWT5.153518.69926.65810.69090.2750
      NSST5.209318.70316.63200.74940.3009
      Lap_cnn4.354820.65486.75480.73560.6548
      CSMCA4.707518.26276.69020.67310.2751
      ECNN3.548726.35476.75480.71540.5487
      Proposed algorithm4.501832.85516.88650.75880.7221
      Image 6DTCWT2.241519.86296.64330.78000.6958
      NSST3.087519.95086.56560.77110.3101
      Lap_cnn3.326820.96546.98540.79841.3548
      CSMCA3.094919.23746.73730.77750.7727
      ECNN3.214818.65786.98540.75460.8547
      Proposed algorithm3.379627.95327.12950.76391.5024
    Tools

    Get Citation

    Copy Citation Text

    Yanchun Yang, Xiaoyu Gao, Jianwu Dang, Yangping Wang. Infrared and Visible Images Fusion Algorithm Based on NSST and IFCNN[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2010004

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image Processing

    Received: Dec. 7, 2020

    Accepted: Jan. 2, 2021

    Published Online: Oct. 12, 2021

    The Author Email: Yang Yanchun (yangyanchun102@sina.com)

    DOI:10.3788/LOP202158.2010004

    Topics