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Current infrared and visible image fusion (IVIF) methods go to great lengthsto excavate complementary features and design complex fusion strategies, whichis 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, whichintegrates both the shared and complementary features from two modalities.Specifically, to skillfully sidestep aggregating complementary information inIVIF, we design a mutual information transfer (MIT) module to mutuallyrepresent features from two modalities, roughly transferring complementaryinformation into harmonious one. Then, a harmonious information acquisitionsupervised by source image (HIASSI) module is devised to further ensure thecomplementary to harmonious information transfer after MIT. Meanwhile, we alsopropose a structure information preservation (SIP) module to guarantee that theedge structure information of the source images can be transferred to thefusion results. Moreover, a mutual promotion training paradigm (MPTP) withinteraction loss is adopted to facilitate better collaboration among MIT,HIASSI and SIP. In this way, the proposed method is able to generate fusedimages with higher qualities. Extensive experimental results demonstrate thesuperiority of our CHITNet over state-of-the-art algorithms in terms of visualquality and quantitative evaluations.

CHITNet: A Complementary to Harmonious Information Transfer Network for Infrared and Visible Image Fusion