In　the　realm　of　deep　learning-based　networks　for　dehazing　using　paired　clean-hazy　image　datasets　to　address　complex　real-world　haze　scenarios　in　daytime　environments　and　cross-dataset　challenges　remains　a　significant　concern　due　to　algorithmic　inefficiencies　and　color　distortion.　To　tackle　these　issues,　we　propose　SwinTieredHazymers　(STH),　a　dehazing　network　designed　to　adaptively　discern　pixel　intensities　in　hazy　images　and　compute　haze　residue　for　clarity　restoration.　Through　a　unique　three-branch　design,　we　hierarchically　modulate　haze　residuals　by　leveraging　the　global　features　brought　by　Transformer　and　the　local　features　brought　by　Convolutional　Neural　Network　(CNN)　which　has　led　to　the　algorithm＇s　widespread　applicability.　Experimental　results　demonstrate　that　our　approach　surpasses　advanced　single-image　dehazing　methods　in　both　quantitative　metrics　and　visual　fidelity　for　real-world　hazy　image　dehazing,　while　also　exhibiting　strong　performance　in　cross-dataset　dehazing　scenarios.