With　the　application　and　popularization　of　depth　cameras,　image　fusion　techniques　based　on　infrared　and　visible　light　are　increasingly　used　in　various　fields.　Object　detection　and　robot　navigation　impose　more　stringent　requirements　on　the　texture　details　and　image　quality　of　fused　images.　Existing　residual　network,　attention　mechanisms,　and　generative　adversarial　network　are　ineffective　in　dealing　with　the　image　fusion　problem　because　of　insufficient　detail　feature　extraction　and　non-conformity　to　the　human　visual　perception　system　during　the　fusion　of　infrared　and　visible　light　images.　Our　newly　developed　RGFusion　network　relies　on　a　two-channel　attentional　mechanism,　a　residual　network,　and　a　generative　adversarial　network　that　introduces　two　new　components:　a　high-precision　image　feature　extractor　and　an　efficient　multi-stage　training　strategy.　The　network　is　preprocessed　by　a　high-dimensional　mapping　and　the　complex　feature　extractor　is　processed　through　a　sophisticated　two-stage　image　fusion　process　to　obtain　feature　structures　with　multiple　features,　resulting　in　high-quality　fused　images　rich　in　detailed　features.　Extensive　experiments　on　public　datasets　validate　this　fusion　approach,　and　RGFusion　is　at　the　forefront　of　SD　metrics　for　EN　and　SF,　reaching　7.366,　13.322,　and　49.281　on　the　TNO　dataset　and　7.276,　19.171,　and　53.777　on　the　RoadScene　dataset,　respectively.　©　2024　Elsevier　B.V.