… deep architectures adopted and fusion scenarios. Then… learning in various types of image
fusion scenarios, including the digital photography image fusion, the multi-modal image fusion …

… In this paper, we propose a general image fusion framework based on the convolutional
neural network, named as IFCNN. Inspired by the transform-domain image fusion algorithms, …

… images as the stumbling block in image fusion, we propose a new unsupervised network for
image fusion. … -driven, the network can be applied to different fusion tasks, ie, it is a unified …

… deepen the network and further extract the image features to … in the sizes of input images
and the network structures of … network based fault recognition method via image fusion of …

… forgetting for continual learning. We develop a new unsupervised network for image fusion
by constraining the similarity between the fusion image and source images to overcome the …

… on network design for image fusion is a meaningful task. The siamese network is employed
for image fusion in this work, but the pseudo-siamese and 2-channel networks are also …

… From a new perspective, we introduce contrastive learning to multi-exposure image fusion.
… the source images and the reference one, which can achieve better fusion performance …

… Moreover, most methods focus on strengthening the network with an increase in depth …
contrastive learning network, dubbed CoCoNet, to realize infrared and visible image fusion in an …

… In this paper, we present deep convolutional neural network for multimodal medical image
fusion. The input of the CNN is shearlet coefficients of a decomposed registered CT and MR …

… images will undoubtedly increase storage burden and time cost. So the following work in this
paper is aimed at extreme exposure image fusion… network to an unsupervised image fusion …