Underwater　image　enhancement　is　a　highly　challenging　task,　requiring　solutions　to　complex　environmental　degradation　factors　such　as　light　attenuation　and　color　cast.　Achieving　stability　in　color　restoration　and　precision　in　texture　recovery　is　key　to　improving　enhancement　results.　However,　existing　methods　generally　lack　in-depth　modeling　of　color　and　texture　information　and　fail　to　efficiently　fuse　these　two　core　visual　components,　significantly　limiting　the　overall　performance　of　the　enhancement　results.　To　this　end,　we　propose　an　innovative　Dual-Attention　Fusion　Net　(DuAF)　that　solves　this　problem.　On　a　global　scale,　DuAF　introduces　explicit　semantic　consistency　constraints　to　precisely　model　color　features　by　reconstructing　pixel　intensity　distribution,　enhancing　sensitivity　to　color　features,　and　capturing　real　pixel　gradient　changes,　effectively　addressing　complex　color　distortion　issues.　On　a　local　scale,　DuAF　dynamically　adjusts　the　perception　window,　combines　optimized　attention　weights　with　positional　deviations,　and　deeply　models　texture　information,　significantly　improving　the　restoration　of　texture　details.　Overall,　DuAF　significantly　improves　the　stability　of　color　restoration　and　the　clarity　of　texture　details　in　complex　degraded　scenes,　providing　an　efficient　and　comprehensive　solution　for　underwater　image　enhancement.　Our　project　is　publicly　available　on　https://github.com/HuShuteng/DuAF.　©　2025　Elsevier　B.V.