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 …

In the image fusion field, the design of deep learning-based fusion methods is far from
routine. It is invariably fusion-task specific and requires a careful consideration. The most …

In this article, we propose a novel method for infrared and visible image fusion where we
develop nest connection-based network and spatial/channel attention models. The nest …

In this paper, we present a novel deep learning architecture for infrared and visible images
fusion problems. In contrast to conventional convolutional networks, our encoding network is …

In recent years, deep learning has become a very active research tool which is used in many
image processing fields. In this paper, we propose an effective image fusion method using a …

Image decomposition is crucial for many image processing tasks, as it allows to extract
salient features from source images. A good image decomposition method could lead to a …

In image fusion approaches, feature extraction and processing are key tasks, and the fusion
performance is directly affected by the different features and processing methods …

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 …

Like bilateral filter (BF), cross bilateral filter (CBF) considers both gray-level similarities and
geometric closeness of the neighboring pixels without smoothing edges, but it uses one …

Infrared and visible image fusion is an important problem in the field of image fusion which
has been applied widely in many fields. To better preserve the useful information from …