Current infrared and visible image fusion (IVIF) methods go to great lengths
to excavate complementary features and design complex fusion strategies, which
is extremely challenging. To this end, we rethink the IVIF outside the box,
proposing a complementary to harmonious information transfer network (CHITNet).
It reasonably transfers complementary information into harmonious one, which
integrates both the shared and complementary features from two modalities.
Specifically, to skillfully sidestep aggregating complementary information in
IVIF, we design a mutual information transfer (MIT) module to mutually
represent features from two modalities, roughly transferring complementary
information into harmonious one. Then, a harmonious information acquisition
supervised by source image (HIASSI) module is devised to further ensure the
complementary to harmonious information transfer after MIT. Meanwhile, we also
propose a structure information preservation (SIP) module to guarantee that the
edge structure information of the source images can be transferred to the
fusion results. Moreover, a mutual promotion training paradigm (MPTP) with
interaction loss is adopted to facilitate better collaboration among MIT,
HIASSI and SIP. In this way, the proposed method is able to generate fused
images with higher qualities. Extensive experimental results demonstrate the
superiority of our CHITNet over state-of-the-art algorithms in terms of visual
quality and quantitative evaluations