Image fusion meets deep learning: A survey and perspective
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 …
from different source images, aims to generate a single image that is more informative and …
A review of multimodal image matching: Methods and applications
Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …
similar structure/content from two or more images that are of significant modalities or …
SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer
This study proposes a novel general image fusion framework based on cross-domain long-
range learning and Swin Transformer, termed as SwinFusion. On the one hand, an attention …
range learning and Swin Transformer, termed as SwinFusion. On the one hand, an attention …
GANMcC: A generative adversarial network with multiclassification constraints for infrared and visible image fusion
Visible images contain rich texture information, whereas infrared images have significant
contrast. It is advantageous to combine these two kinds of information into a single image so …
contrast. It is advantageous to combine these two kinds of information into a single image so …
Lrrnet: A novel representation learning guided fusion network for infrared and visible images
Deep learning based fusion methods have been achieving promising performance in image
fusion tasks. This is attributed to the network architecture that plays a very important role in …
fusion tasks. This is attributed to the network architecture that plays a very important role in …
Current advances and future perspectives of image fusion: A comprehensive review
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 …
world than a single modality alone. Infrared images discriminate targets with respect to their …
Deep learning-based multi-focus image fusion: A survey and a comparative study
X Zhang - IEEE Transactions on Pattern Analysis and Machine …, 2021 - ieeexplore.ieee.org
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 …
learning has been introduced to the field of MFIF and various methods have been proposed …
MUFusion: A general unsupervised image fusion network based on memory unit
Existing image fusion approaches are committed to using a single deep network to solve
different image fusion problems, achieving promising performance in recent years. However …
different image fusion problems, achieving promising performance in recent years. However …
Fusion from decomposition: A self-supervised decomposition approach for image fusion
Image fusion is famous as an alternative solution to generate one high-quality image from
multiple images in addition to image restoration from a single degraded image. The essence …
multiple images in addition to image restoration from a single degraded image. The essence …
Rfnet: Unsupervised network for mutually reinforcing multi-modal image registration and fusion
In this paper, we propose a novel method to realize multi-modal image registration and
fusion in a mutually reinforcing framework, termed as RFNet. We handle the registration in a …
fusion in a mutually reinforcing framework, termed as RFNet. We handle the registration in a …