Underwater　images　serve　as　crucial　mediums　for　conveying　marine　information.　Nevertheless,　due　to　the　inherent　complexity　of　the　underwater　environment,　underwater　images　often　suffer　from　various　quality　degradation　phenomena　such　as　color　deviation,　low　contrast,　and　non-uniform　illumination.　These　degraded　underwater　images　fail　to　meet　the　requirements　of　underwater　computer　vision　applications.　Consequently,　effective　quality　optimization　of　underwater　images　is　of　paramount　research　and　analytical　value.　Based　on　whether　they　rely　on　underwater　physical　imaging　models,　underwater　image　quality　optimization　techniques　can　be　categorized　into　underwater　image　enhancement　and　underwater　image　restoration　methods.　This　paper　provides　a　comprehensive　review　of　underwater　image　enhancement　and　restoration　algorithms,　accompanied　by　a　brief　introduction　to　underwater　imaging　model.　Then,　we　systematically　analyze　publicly　available　underwater　image　datasets　and　commonly-used　quality　assessment　methodologies.　Furthermore,　extensive　experimental　comparisons　are　carried　out　to　assess　the　performance　of　underwater　image　optimization　algorithms　and　their　practical　impact　on　high-level　vision　tasks.　Finally,　the　challenges　and　future　development　trends　in　this　field　are　discussed.　We　hope　that　the　efforts　made　in　this　paper　will　provide　valuable　references　for　future　research　and　contribute　to　the　innovative　advancement　of　underwater　image　optimization.