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 …

Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

The necessity of image fusion is growing in recently in image processing applications due to
the tremendous amount of acquisition systems. Fusion of images is defined as an alignment …

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

In this paper, we propose a general image fusion framework based on the convolutional
neural network, named as IFCNN. Inspired by the transform-domain image fusion …

Image fusion integrates a series of images acquired from different sensors, eg, infrared and
visible, outputting an image with richer information than either one. Traditional and recent …

As an effective way to integrate the information contained in multiple medical images with
different modalities, medical image fusion has emerged as a powerful technique in various …

Multimodality medical imaging techniques have been increasingly applied in clinical
practice and research studies. Corresponding multimodal image analysis and ensemble …

Pixel-level image fusion is designed to combine multiple input images into a fused image,
which is expected to be more informative for human or machine perception as compared to …

Multi-modality image fusion provides more comprehensive and sophisticated information in
modern medical diagnosis, remote sensing, video surveillance, and so on. This paper …