The existing transformer-based infrared and visible image fusion methods mainly focus on
the self-attention correlation existing in the intra-modal of each image; yet these methods …

Image segmentation is a fundamental building block of automatic medical applications. It
has been greatly improved since the emergence of deep neural networks. However, deep …

Long-wave infrared (thermal) images distinguish the target and background according to
different thermal radiation. They are insensitive to light conditions, and cannot present …

In this paper, a robust infrared and visible image fusion scheme that joins a dual-branch
multi-receptive-field neural network and a color vision transfer algorithm is designed to …

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 …

Infrared images contain salient target information and visible images contain texture
information. The fusion of infrared and visible images makes images express better visual …

Due to the discrepancy between training and test data, autoencoder-based image fusion
methods trained by natural images easily lose vital information of infrared and visible …

In order to be adapted to high-level vision tasks, several infrared and visible image fusion
methods cascade with the downstream network to enhance the semantic information of …

Infrared and visible image fusion is an important branch in the field of information fusion,
which aims to integrate effective information from different sensors and enhance the integrity …

In cell and molecular biology, the fusion of green fluorescent protein (GFP) and phase
contrast (PC) images aims to generate a composite image, which can simultaneously …