• Laser & Optoelectronics Progress
  • Vol. 58, Issue 20, 2010004 (2021)
Yanchun Yang*, Xiaoyu Gao, Jianwu Dang, and Yangping Wang
Author Affiliations
  • School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China
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    DOI: 10.3788/LOP202158.2010004 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    Decomposition process of NSST
    Fig. 1. Decomposition process of NSST
    Framework of IFCNN
    Fig. 2. Framework of IFCNN
    Demonstration of feature extraction and feature fusion. (a) Infrared images; (b) visible images; (c) fusion images
    Fig. 3. Demonstration of feature extraction and feature fusion. (a) Infrared images; (b) visible images; (c) fusion images
    Processing flow of proposed algorithm
    Fig. 4. 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
    Fig. 5. 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
    Fig. 6. 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
    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
    Table 1. Objective evaluation index of different algorithms on different images
    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
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