Multimodal medical image fusion consists in combining two or more images of the same or
different modalities aiming to improve the image content, and preserve information. The …

Multi-focus image fusion is an effective technique to extend the depth-of-field of optical
lenses by creating an all-in-focus image from a set of partially focused images of the same …

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 …

The necessity of image fusion is growing in recently in image processing applications due to
the tremendous amount of acquisition systems. Fusion of images is defined as an alignment …

Infrared images can distinguish targets from their backgrounds based on the radiation
difference, which works well in all-weather and all-day/night conditions. By contrast, visible …

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 …

At the crossing of the statistical and functional analysis, there exists a relentless quest for an
efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising …

Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …

In this study, we propose a target-enhanced multiscale transform (MST) decomposition
model for infrared and visible image fusion to simultaneously enhance the thermal target in …

This study proposes a novel unsupervised network for IR/VIS fusion task, termed as
RXDNFuse, which is based on the aggregated residual dense network. In contrast to …