Owing to the limitations of imaging sensors, it is challenging to obtain a medical image that
simultaneously contains functional metabolic information and structural tissue details …

In recent times, several medical image fusion techniques based on the convolutional neural
network (CNN) have been proposed for various medical imaging fusion tasks. However …

At present, multimodal medical image fusion technology has become an essential means for
researchers and doctors to predict diseases and study pathology. Nevertheless, how to …

Medical image fusion technology synthesizes complementary information from multimodal
medical images. This technology is playing an increasingly important role in clinical …

In recent years, extensive multimodal medical image fusion algorithms have been proposed.
However, existing methods are primarily based on specific transformation theories. There …

The main purpose of multimodal medical image fusion is to aggregate the significant
information from different modalities and obtain an informative image, which provides …

Infrared and visible image fusion targets to provide an informative image by combining
complementary information from different sensors. Existing learning-based fusion …

Recently, deep learning has been widely used in the imaging field. Residual, pyramid and
attention networks are proposed successively, and are extensively used because of their …

Existing image fusion methods always use the same representations for different modal
medical images. Otherwise, they solve the fusion problem by subjectively defining …

The image fusion methods based on deep learning has become a research hotspot in the
field of computer vision in recent years. This paper reviews these methods from five aspects …