  Infrared and visible images, as multi-modal image pairs, show significant
differences in the expression of the same scene. The image fusion task is faced
with two problems: one is to maintain the unique features between different
modalities, and the other is to maintain features at various levels like local
and global features. This paper discusses the limitations of deep learning
models in image fusion and the corresponding optimization strategies. Based on
artificially designed structures and constraints, we divide models into
explicit models, and implicit models that adaptively learn high-level features
or can establish global pixel associations. Ten models for comparison
experiments on 21 test sets were screened. The qualitative and quantitative
results show that the implicit models have more comprehensive ability to learn
image features. At the same time, the stability of them needs to be improved.
Aiming at the advantages and limitations to be solved by existing algorithms,
we discuss the main problems of multi-modal image fusion and future research
directions.
