In　this　article,　we　introduce　a　novel　Frequency-Aware　Infrared　and　Visible　Image　Fusion　Network　(FAFusion)　designed　to　explore　both　low　and　high-frequency　information　present　in　infrared　and　visible　images.　Our　approach　involves　leveraging　the　low-frequency　information,　which　preserves　the　contour　and　overall　brightness　of　the　source　images,　in　the　encoder.　This　enables　FAFusion　to　prioritize　salient　targets.　Simultaneously,　we　feed　the　high-frequency　information,　preserving　object　edges　and　texture　details,　into　the　decoder,　enhancing　the　fused　images　with　richer　details.　To　maintain　the　frequency　information　from　the　source　images,　we　propose　a　maximum　frequency　loss　function.　This　function　considers　the　frequency　components　between　the　fused　image　and　the　source　images,　ensuring　the　preservation　of　critical　frequency　details.　Additionally,　we　introduce　a　Residual　Multiscale　Feature　Extraction　(RMFE)　module　to　capture　diverse　contextual　information　from　the　source　images.　The　resulting　FAFusion　demonstrates　the　capability　to　generate　fused　images　that　exhibit　both　rich　texture　details　and　emphasize　salient　targets.　We　validate　the　effectiveness　of　our　approach　through　extensive　experiments　on　three　publicly　available　datasets　(TNO,　MSRS,　and　M3FD).　Comparative　analyses　against　nine　state-of-the-art　methods　highlight　the　superior　visual　effects,　quantitative　metrics,　and　generalization　performance　achieved　by　the　proposed　FAFusion.The　source　codes　will　be　available　at　https://github.com/guobaoxiao/FAFusion.