Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …

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 …

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 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 …

In this paper, we present a new unsupervised and unified densely connected network for
different types of image fusion tasks, termed as FusionDN. In our method, the densely …

Multi-focus image fusion (MFIF) is an important area in image processing. Since 2017, deep
learning has been introduced to the field of MFIF and various methods have been proposed …

Multi-focus image fusion is an enhancement method to generate full-clear images, which
can address the depth-of-field limitation in imaging of optical lenses. Most existing methods …

We present a novel deep learning architecture for fusing static multi-exposure images.
Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input …