In　complex　underwater　environments,　the　image　quality　will　be　damaged　due　to　light　absorption,　water　quality　and　other　factors,　which　brings　certain　challenges　to　the　underwater　object　detection　task.　In　order　to　improve　the　quality　of　under-water　images,　as　well　as　the　effectiveness　of　underwater　detection　tasks,　we　proposed　a　multi-reference　mapping　based　image　enhancement　network　(MRUIE).　Previous　image　enhancement　methods　set　only　one　reference　image　for　the　original　image　to　be　learned,　ignoring　the　diversity　of　real　underwater　images.　We　optimized　for　the　salient　problems　of　underwater　images　by　allowing　the　network　to　learn　multiple　reference　images　to　capture　the　uncertainty　of　real　underwater　images.　First,　for　the　prominent　problems　of　color　distortion,　contrast　imbalance,　and　low　brightness　and　darkness　in　underwater　images,　we　designed　three　optimization　paths　to　generate　three　reference　images　respectively.　Then,　the　three　reference　images　are　fed　into　the　feature　extraction　network　to　construct　the　statistical　distribution　of　features　and　generate　a　series　of　potential　enhancement　distributions.　Finally,　based　on　the　enhancement　distributions,　Monte　Carlo　likelihood　estimation　is　used　to　determine　the　final　enhancement　results.　Experiments　conducted　on　two　datasets　demonstrate　that　our　proposed　image　enhancement　algorithm　can　effectively　enhance　the　restoration　of　the　original　underwater　images　and　provide　significant　performance　improvement　in　underwater　vision　tasks.　©　2024　IEEE.