Image fusion, which refers to extracting and then combining the most meaningful information
from different source images, aims to generate a single image that is more informative and …

Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

This study proposes a novel general image fusion framework based on cross-domain long-
range learning and Swin Transformer, termed as SwinFusion. On the one hand, an attention …

As a vital image enhancement technology, infrared and visible image fusion aims to
generate high-quality fused images with salient targets and abundant texture in extreme …

Visible and infrared image fusion (VIF) has attracted a lot of interest in recent years due to its
application in many tasks, such as object detection, object tracking, scene segmentation …

The existing deep learning fusion methods mainly concentrate on convolutional neural
networks (CNNs), and few attempts are made with transformer. Meanwhile, the …

A good result of infrared and visible image fusion should not only maintain significant
contrast for distinguishing targets from the backgrounds, but also contain rich scene textures …

Color plays an important role in human visual perception, reflecting the spectrum of objects.
However, the existing infrared and visible image fusion methods rarely explore how to …

Infrared and visible image fusion intends to generate a synthetic image with superior scene
representation and better visual perception. The existing deep learning-based fusion …

In this article, we propose a novel decomposition method by applying disentangled
representation for visible and infrared image fusion (DRF). According to the imaging …