Fusing　infrared　and　visible　imagery　aims　to　harness　their　complementary　spectral　data,　enhancing　output　image　quality,　sharpness,　and　content.　However,　convolutional　neural　networks　(CNNs)　are　not　capable　of　capturing　long-range　dependencies,　while　the　Transformer　architecture　faces　the　challenge　of　consuming　huge　computational　resources.　To　address　these　critical　challenges,　this　paper　proposes　a　novel　framework　named　MBHFuse,　which　employs　a　multi-branch　heterogeneous　global　and　local　image　fusion　approach.　To　effectively　extract　global　and　local　features,　we　design　a　multi-branch　heterogeneous　encoder　module　and　introduce　a　differential　convolution　amplification　module　(DCAM)　to　further　extract　complementary　information.　Additionally,　we　devise　a　new　loss　function,　incorporating　multi-branch　feature　decomposition　loss,　intensity　loss,　gradient　loss,　mean　squared　error　loss,　and　structural　similarity　loss,　for　training　the　proposed　MBHFuse　model.　Through　extensive　experiments　on　public　datasets,　we　demonstrate　that　the　proposed　framework　outperforms　other　stateof-the-art　(SOTA)　methods　in　both　qualitative　and　quantitative　evaluations.　Our　code　will　be　available　at　htt　ps://github.com/sunyichen1994/MBHFuse.