  General deep learning-based methods for infrared and visible image fusion
rely on the unsupervised mechanism for vital information retention by utilizing
elaborately designed loss functions. However, the unsupervised mechanism
depends on a well designed loss function, which cannot guarantee that all vital
information of source images is sufficiently extracted. In this work, we
propose a novel interactive feature embedding in self-supervised learning
framework for infrared and visible image fusion, attempting to overcome the
issue of vital information degradation. With the help of self-supervised
learning framework, hierarchical representations of source images can be
efficiently extracted. In particular, interactive feature embedding models are
tactfully designed to build a bridge between the self-supervised learning and
infrared and visible image fusion learning, achieving vital information
retention. Qualitative and quantitative evaluations exhibit that the proposed
method performs favorably against state-of-the-art methods.
