The　purpose　of　infrared　and　visible　(Inf-Vis)　image　fusion　is　to　merge　images　from　two　different　wavelength　cameras　into　a　fused　image,　aiming　to　obtain　more　information　and　richer　visual　content　than　a　single　image　can　provide.　To　be　able　to　better　extract　the　local　key　information　in　the　image,　and　reduce　the　redundant　information　in　the　image,　this　article　advances　a　hybrid　attention-based　fusion　algorithm　for　illumination-aware　Inf-Vis　images　(HAIAFusion).　Specifically,　we　introduce　a　DenseNet-201-based　illumination-aware　subnetwork,　and　we　also　propose　a　multimodal　differential　perception　fusion　module　based　on　a　hybrid　attention　(MDP-HA)　that　integrates　channel　attention,　position　attention,　and　corner　attention　(COA)　in　a　cascade　fashion.　Through　extensive　simulation　experiments,　our　algorithm　outperforms　state-of-the-art　(SOTA)　methods　in　terms　of　performance.　The　generated　fused　images　are　richer　in　information,　have　higher　contrast,　and　are　closer　to　human　visual　perception.　Furthermore,　this　approach　enhances　the　preservation　of　the　source　image＇s　edge　information.　All　these　outcomes　demonstrate　the　significant　potential　of　the　HAIAFusion　algorithm　within　the　domain　of　illumination-aware　Inf-Vis　image　fusion.　This　algorithm　offers　notable　performance　improvements　for　achieving　various　related　visual　tasks.　Our　code　will　be　available　at　https://github.com/sunyichen1994/HAIAFusion.git.