Infrared and Laser Engineering, Volume. 51, Issue 4, 20210291(2022)
Infrared and visible image fusion using improved generative adversarial networks
Figures & Tables(8)
Fig. 5. Comparative experimental results of the RoadScene dataset. (a) Infrared image；(b) Visible image；(c) DRTV；(d) CNN；(e) FusionGAN；(f) DIDFuse；(g) DDcGAN；(h) Proposed method
Table 1. Objective evaluation results of two comparison experiment
View tableView in ArticleTable 1. Objective evaluation results of two comparison experiment
Dataset Methods AG SF ${Q^{AB/F}}$ ${Q_{CB}}$ TNO DRTV 3.761 9.639 0.319 0.411 CNN 4.700 11.489 0.332 0.463 FusionGAN 4.014 10.006 0.313 0.425 DIDFuse 4.644 11.771 0.395 0.472 DDcGAN 5.529 13.044 0.356 0.456 Proposed method 5.861 14.454 0.401 0.504 Road scene DRTV 3.221 8.696 0.368 0.384 CNN 4.484 10.536 0.398 0.384 FusionGAN 3.290 8.426 0.278 0.387 DIDFuse 5.253 14.149 0.469 0.452 DDcGAN 5.200 13.580 0.423 0.461 Proposed method 5.855 15.243 0.480 0.494
Table 2. Evaluation results of ablation experiment
View tableView in ArticleTable 2. Evaluation results of ablation experiment
Methods AG SF ${Q^{AB/F}}$ ${Q_{CB}}$ DDcGAN 5.529 13.044 0.356 0.456 DDcGAN+SN 5.670 13.062 0.368 0.469 DDcGAN+GSFB 5.746 13.841 0.388 0.484 DDcGAN+GSFB+SN 5.861 14.454 0.401 0.504
Tools
Get Citation
Copy Citation Text
Li Min, Sijian Cao, Huaici Zhao, Pengfei Liu. Infrared and visible image fusion using improved generative adversarial networks[J]. Infrared and Laser Engineering, 2022, 51(4): 20210291
Paper Information
Category: Image processing
Received: May. 2, 2021
Accepted: --
Published Online: May. 18, 2022
The Author Email: Zhao Huaici (hczhao@sia.cn)
© Copyright 2018-2021 | Chinese Laser Press.
All Rights Reserved 沪ICP备15018463号-20