Background and objectives Over the past two decades, medical imaging has been
extensively apply to diagnose diseases. Medical experts continue to have difficulties for …

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 proposed a new end-to-end model, termed as dual-discriminator
conditional generative adversarial network (DDcGAN), for fusing infrared and visible images …

Multimodal medical image fusion consists in combining two or more images of the same or
different modalities aiming to improve the image content, and preserve information. The …

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

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 …

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 …

The recent years have been marked by continuous improvements of remote sensors with
applications like monitoring and management of the environment, precision agriculture …

The approach of multimodal medical image fusion, which extracts complementary
information from several multimodality medical pictures, is one of the most significant and …

In this letter, a sparse representation (SR) model named convolutional sparsity based
morphological component analysis (CS-MCA) is introduced for pixel-level medical image …