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 …

Multi-modality is widely used in medical imaging, because it can provide multiinformation
about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing …

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 …

In the image fusion field, the design of deep learning-based fusion methods is far from
routine. It is invariably fusion-task specific and requires a careful consideration. The most …

In this paper, a squeeze-and-decomposition network (SDNet) is proposed to realize multi-
modal and digital photography image fusion in real time. Firstly, we generally transform …

This study proposes a novel unified and unsupervised end-to-end image fusion network,
termed as U2Fusion, which is capable of solving different fusion problems, including multi …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

Visible images contain rich texture information, whereas infrared images have significant
contrast. It is advantageous to combine these two kinds of information into a single image so …

In this article, we propose a novel method for infrared and visible image fusion where we
develop nest connection-based network and spatial/channel attention models. The nest …

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 …