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 …

Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …

Multi-modality (MM) image fusion aims to render fused images that maintain the merits of
different modalities, eg, functional highlight and detailed textures. To tackle the challenge in …

Image fusion aims to integrate complementary information in source images to synthesize a
fused image comprehensively characterizing the imaging scene. However, existing image …

Infrared and visible image fusion aims to synthesize a single fused image containing salient
targets and abundant texture details even under extreme illumination conditions. However …

Infrared and visible image fusion aims to synthesize a single fused image that not only
contains salient targets and abundant texture details but also facilitates high-level vision …

Multi-modality image fusion aims to combine different modalities to produce fused images
that retain the complementary features of each modality, such as functional highlights and …

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 …

Deep learning based fusion methods have been achieving promising performance in image
fusion tasks. This is attributed to the network architecture that plays a very important role in …