Image fusion aims to integrate complementary characteristics of source images into a single
fused image that better serves human visual observation and machine vision perception …

Masked image modeling (MIM) is a highly popular and effective self-supervised learning
method for image understanding. The existing MIM-based methods mostly focus on spatial …

Multimodal image fusion aims to integrate information from different imaging techniques to
produce a comprehensive, detail-rich single image for downstream vision tasks. Existing …

Image fusion techniques aim to generate more informative images by merging multiple
images of different modalities with complementary information. Despite significant fusion …

Background The approaches based on vision transformers (ViTs) are advancing the field of
medical artificial intelligence (AI) and cancer diagnosis. Recently, many researchers have …

Remote sensing image change detection is extensively utilized in various applications in the
field of remote sensing, particularly in the realm of cropland conservation, where it plays a …

Deep learning based fusion mechanisms have achieved sophisticated performance in the
field of image fusion. However, most existing approaches focus on learning global and local …

Infrared-visible image fusion (IVIF) has attracted much attention owing to the highly-
complementary properties of the two image modalities. Due to the lack of ground-truth fused …

Infrared and visible image fusion (IVIF) strives to render fused results that preserve the
strengths of the source images (eg, texture details and thermal highlights) while boosting …

Existing deep learning-based methods often follow either image-level or feature-level fusion
frameworks to uniformly or separately extract features, ignoring the specialized interactive …