  Deep learning-based image fusion approaches have obtained wide attention in
recent years, achieving promising performance in terms of visual perception.
However, the fusion module in the current deep learning-based methods suffers
from two limitations, \textit{i.e.}, manually designed fusion function, and
input-independent network learning. In this paper, we propose an unsupervised
adaptive image fusion method to address the above issues. We propose a feature
mutual mapping fusion module and dual-branch multi-scale autoencoder. More
specifically, we construct a global map to measure the connections of pixels
between the input source images. % The found mapping relationship guides the
image fusion. Besides, we design a dual-branch multi-scale network through
sampling transformation to extract discriminative image features. We further
enrich feature representations of different scales through feature aggregation
in the decoding process. Finally, we propose a modified loss function to train
the network with efficient convergence property. Through sufficient training on
infrared and visible image data sets, our method also shows excellent
generalized performance in multi-focus and medical image fusion. Our method
achieves superior performance in both visual perception and objective
evaluation. Experiments prove that the performance of our proposed method on a
variety of image fusion tasks surpasses other state-of-the-art methods, proving
the effectiveness and versatility of our approach.
