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 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 …

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 …

To enhance the confidentiality and the robustness of double image encryption algorithms, a
novel double-image compression-encryption algorithm is proposed by combining co-sparse …

Among the representation learning, the low-rank representation (LRR) is one of the hot
research topics in many fields, especially in image processing and pattern recognition …

In this paper, a new double color images encryption algorithm is introduced based on 2D
compressive sensing and wavelet basis, which can realize image encryption and …

In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for
epileptic electroencephalogram (EEG) signal classification. The goal is to develop a …