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 …

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 …

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 …

Visible and infrared image fusion is an important area in image processing due to its
numerous applications. While much progress has been made in recent years with efforts on …

In this paper, a novel deep network is proposed for multi-focus image fusion, named Deep
Regression Pair Learning (DRPL). In contrast to existing deep fusion methods which divide …

Multi-exposure image fusion (MEF) is an important area in computer vision and has attracted
increasing interests in recent years. Apart from conventional algorithms, deep learning …

Due to the huge gap between the high dynamic range of natural scenes and the limited
(low) range of consumer-grade cameras, a single-shot image can hardly record all the …

This paper proposes an unsupervised multi-exposure image fusion (MEF) method via
contrastive learning, termed as MEF-CL. It breaks exposure limits and performance …

In this paper, a novel fusion network for infrared and visible images is proposed, named
Image Pair Learning Fusion Network (IPLF). At present, most of the released deep learning …

We aim to address the challenging task of infrared and visible image fusion. The existed
fusion methods cannot achieve the balance of clear boundaries and rich details. In this …