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 …

Image fusion synthesizes a new image from multiple images of the same scene. The
synthesized image should be suitable for human visual perception and follow-up high-level …

In this paper, we propose a novel unsupervised continual-learning generative adversarial
network for unified image fusion, termed as UIFGAN. In our model, for multiple image fusion …

This study proposes a unified gradient-and intensity-discriminator generative adversarial
network for various image fusion tasks, including infrared and visible image fusion, medical …

Generative adversarial network (GAN) has shown great potential in infrared and visible
image fusion. The existing GAN-based methods establish an adversarial game between …

In this paper, we present an end-to-end architecture for multi-exposure image fusion based
on generative adversarial networks, termed as MEF-GAN. In our architecture, a generator …

Infrared images can distinguish targets from their backgrounds on the basis of difference in
thermal radiation, which works well at all day/night time and under all weather conditions. By …

In this paper, we propose a new end-to-end model, called dual-discriminator conditional
generative adversarial network (DDcGAN), for fusing infrared and visible images of different …

We aim to address the challenging task of infrared and visible image fusion. The existed
fusion methods cannot achieve the balance of clear boundaries and rich details. In this …

Deep learning is a rapidly developing approach in the field of infrared and visible image
fusion. In this context, the use of dense blocks in deep networks significantly improves the …