Yang Yang, Zhennan Ren, Beichen Li. Infrared and Visible Image Fusion with Convolutional Neural Network and Transformer[J]. Laser & Optoelectronics Progress, 2023, 60(16): 1610013
- Laser & Optoelectronics Progress
- Vol. 60, Issue 16, 1610013 (2023)
Fig. 1. Overall structure of the proposed model
Fig. 2. Concrete structure of the Encoder
Fig. 3. Fusion strategy
Fig. 4. Fusion results of the “Street” image. (a) Infrared image; (b) visible image; (c) RP; (d) Wavelet; (e) ResNet-ZCA; (f) DenseFuse; (g) Dual-Branch; (h) FusionGAN; (i) GANMcC; (j) proposed method
Fig. 5. Fusion results of the “Kaptein_1123” image. (a) Infrared image; (b) visible image; (c) RP; (d) Wavelet; (e) ResNet-ZCA; (f) DenseFuse; (g) Dual-Branch; (h) FusionGAN; (i) GANMcC; (j) proposed method
Fig. 6. Fusion results of the “Kaptein_1654” image. (a) Infrared image; (b) visible image; (c) RP; (d) Wavelet; (e) ResNet-ZCA; (f) DenseFuse; (g) Dual-Branch; (h) FusionGAN; (i) GANMcC; (j) proposed method
Fig. 7. Fusion results of the “Bunker” image. (a) Infrared image; (b) visible image; (c) RP; (d) Wavelet; (e) ResNet-ZCA; (f) DenseFuse; (g) Dual-Branch; (h) FusionGAN; (i) GANMcC; (j) proposed method
Fig. 8. Six objective metrics of different fusion models on TNO dataset. (a) En; (b) SD; (c) SF; (d) MI; (e) SCD; (f) Q_abf
Fig. 9. Subjective results of the ablation experiment
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Table 1. Average value of objective results of the ablation experiment
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