  As a common image processing technique, image decomposition is often used to
extract complementary information between modalities. In current
decomposition-based image fusion methods, typically, source images are
decomposed into three parts at single scale (i.e., visible-exclusive part,
infrared-exclusive part, and common part) and lacking interaction between
modalities during the decomposition process. These results in the inability of
fusion images to effectively focus on finer complementary information between
modalities at various scales. To address the above issue, a novel decomposition
mechanism, Continuous Decomposition Fusion (CDeFuse), is proposed. Firstly,
CDeFuse extends the original three-part decomposition to a more general K-part
decomposition at each scale through similarity constraints to fuse multi-scale
information and achieve a finer representation of decomposition features.
Secondly, a Continuous Decomposition Module (CDM) is introduced to assist
K-part decomposition. Its core component, State Transformer (ST), efficiently
captures complementary information between modalities by utilizing multi-head
self-attention mechanism. Finally, a novel decomposition loss function and the
corresponding computational optimization strategy are utilized to ensure the
smooth progress of the decomposition process while maintaining linear growth in
time complexity with the number of decomposition results K. Extensive
experiments demonstrate that our CDeFuse achieves comparable performance
compared to previous methods. The code will be publicly available.
