Underwater　image　enhancement　has　been　widely　used　and　received　increasing　attention.　However,　visual　perception　is　affected　by　environmental　factors,　which　reduces　the　visual　quality.　These　images　often　suffer　from　color　distortion,　low　contrast,　and　lack　of　detail,　and　the　color　of　the　image　turns　green　or　blue.　This　paper　proposes　an　underwater　image　enhancement　method　based　on　deep　learning　and　establishes　a　neural　network　based　on　dilated　convolution　and　parameter　correction.　Firstly,　multi-scale　features　are　extracted,　and　then　global　features　are　used　to　enhance　local　features　at　each　scale.　In　addition,　the　innovative　CBAM　attention　mechanism　is　introduced.　The　algorithm　is　significantly　faster　than　ICM,　RGHS,　UCM,　CLAHE,　HE,　etc.,　in　terms　of　operation　speed.　Besides,　better　computational　performance,　color　correction,　and　detail　retention　are　obtained.　©　2022　IEEE.