DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion
Wuhan University · Harbin Institute of Technology · +1 more institution
Abstract
In this paper, we proposed a new end-to-end model, termed as dual-discriminator conditional generative adversarial network (DDcGAN), for fusing infrared and visible images of different resolutions. Our method establishes an adversarial game between a generator and two discriminators. The generator aims to generate a real-like fused image based on a specifically designed content loss to fool the two discriminators, while the two discriminators aim to distinguish the structure differences between the fused image and two source images, respectively, in addition to the content loss. Consequently, the fused image is forced to simultaneously keep the thermal radiation in the infrared image and the texture details in…
Citation impact
- FWCI
- 101.36
- Percentile
- 100%
- References
- 68
Authors
5Topics & keywords
- Discriminator
- Artificial intelligence
- Dual (grammatical number)
- Image fusion
- Computer science
- Generative adversarial network
- Adversarial system
- Image (mathematics)
- Reduced inequalities