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 …

Image fusion aims to combine information from different source images to create a
comprehensively representative image. Existing fusion methods are typically helpless in …

Computational hyperspectral imaging (CHI) is a cutting-edge technique, which plays a
pivotal role in breaking through the quality bottleneck of hyperspectral images (HSI). Among …

Artificial Intelligence (AI) has revolutionized medical imaging procedures, specifically with
regard to image segmentation, reconstruction, interpretation, and research. 3D Slicer, an …

The current constraint rules of end-to-end infrared and visible image fusion (IVIF) networks
based on deep learning solely focus on the pixel level, disregarding the consideration of …

In recent years, deep learning research has received significant attention in the field of
infrared and visible image fusion. However, the issue of designing loss functions in deep …

In recent years, although significant progress has been made in infrared and visible image
fusion, existing methods typically assume that the source images have been rigorously …

Integrating complementary information from different modalities is one of the key challenges
in image fusion. Most of the existing deep learning-based methods still rely on a one-off …

The image fusion community is thriving with the wave of deep learning, and the most
popular fusion methods are usually built upon well-designed network structures. However …

Multimodal data fusion plays an increasingly important role in the field of artificial
intelligence. The objective of Infrared and Visible Image Fusion (IVF) is to integrate …