In this paper, a squeeze-and-decomposition network (SDNet) is proposed to realize multi-
modal and digital photography image fusion in real time. Firstly, we generally transform …

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 this paper, we propose TransMEF, a transformer-based multi-exposure image fusion
framework that uses self-supervised multi-task learning. The framework is based on an …

The end-to-end image fusion framework has achieved promising performance, with
dedicated convolutional networks aggregating the multi-modal local appearance. However …

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 …

In this study, we propose a novel RGB-T tracking framework by jointly modeling both
appearance and motion cues. First, to obtain a robust appearance model, we develop a …

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 …