Image fusion, which refers to extracting and then combining the most meaningful information
from different source images, aims to generate a single image that is more informative and …

Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

Multi-focus image fusion (MFIF) is an important area in image processing. Since 2017, deep
learning has been introduced to the field of MFIF and various methods have been proposed …

Multi-focus image fusion (MFF) is an effective way to eliminate the out-of-focus blur
generated in the imaging process. The difficulties in distinguishing different blur levels and …

The image fusion methods based on deep learning has become a research hotspot in the
field of computer vision in recent years. This paper reviews these methods from five aspects …

We consider a deep learning model for classifying high-dimensional data and seek to
achieve optimal evaluation accuracy and robustness based on multicriteria decision-making …

Multi-focus image fusion methods can be mainly divided into two categories: transform
domain methods and spatial domain methods. Recent emerged deep learning (DL)-based …

Multi-focus image fusion (MFIF) attempts to achieve an “all-focused” image from multiple
source images with the same scene but different focused objects. Given the lack of multi …

Compared with the traditional remote sensing image, there is a large amount of spectral
information in the hyperspectral image (HSI), which makes HSI better reflect the actual …

Generating the decision map with accurate boundaries is the key to fusing multi-focus
images. In this paper, we introduce edge-preservation (EP) techniques into neural networks …