  In recent years, various applications in computer vision have achieved
substantial progress based on deep learning, which has been widely used for
image fusion and shown to achieve adequate performance. However, suffering from
limited ability in modeling the spatial correspondence of different source
images, it still remains a great challenge for existing unsupervised image
fusion models to extract appropriate feature and achieves adaptive and balanced
fusion. In this paper, we propose a novel cross-attention-guided image fusion
network, which is a unified and unsupervised framework for multi-modal image
fusion, multi-exposure image fusion, and multi-focus image fusion. Different
from the existing self-attention module, our cross-attention module focus on
modeling the cross-correlation between different source images. Using the
proposed cross attention module as a core block, a densely connected cross
attention-guided network is built to dynamically learn the spatial
correspondence to derive better alignment of important details from different
input images. Meanwhile, an auxiliary branch is also designed to model the
long-range information, and a merging network is attached to finally
reconstruct the fusion image. Extensive experiments have been carried out on
publicly available datasets, and the results demonstrate that the proposed
model outperforms the state-of-the-art quantitatively and qualitatively.
