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

Infrared and visible image fusion intends to generate a synthetic image with superior scene
representation and better visual perception. The existing deep learning-based fusion …

Combining information from multispectral images into a fused image is informative and
beneficial for human or machine perception. Currently, multiple photodetectors with different …

The images acquired by a single visible light sensor are very susceptible to light conditions,
weather changes, and other factors, while the images acquired by a single infrared light …

Visible and infrared image fusion is an important area in image processing due to its
numerous applications. While much progress has been made in recent years with efforts on …

As a powerful assistance technique for biomedical diagnosis, multimodal medical image
fusion has emerged as a hot topic in recent years. Unfortunately, the trade-off among fusion …

This study proposes a novel unsupervised network for IR/VIS fusion task, termed as
RXDNFuse, which is based on the aggregated residual dense network. In contrast to …

Infrared and visible image fusion aims to obtain a synthetic image that can simultaneously
exhibit salient objects and provide abundant texture details. However, existing deep …

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

Abstract The Convolutional Neural Networks (CNNs) based multi-focus image fusion
methods have recently attracted enormous attention. They greatly enhanced the constructed …