Convolutional　neural　networks　(CNNs)　are　employed　to　achieve　the　color　balance　and　dehazing　of　degraded　underwater　images.　In　the　module　of　color　balance,　an　underwater　generative　adversarial　network　(UGAN)　is　constructed.　The　mapping　relationship　between　underwater　images　with　color　deviation　and　clean　underwater　images　is　learned.　In　the　module　of　clarity　improvement,　an　all-in-one　dehazing　model　is　proposed　in　which　a　comprehensive　index　is　introduced　and　estimated　by　deep　CNN.　The　third　module　to　enhance　underwater　images　adopts　an　adaptive　contrast　improvement　method　by　fusing　global　and　local　histogram　information.　Combined　with　several　underwater　image　datasets,　the　proposed　enhancement　method　based　on　the　three　modules　is　evaluated,　both　by　subjective　visual　effects　and　quantitative　evaluation　metrics.　To　demonstrate　the　advantages　of　the　proposed　method,　several　commonly　used　underwater　image　enhancement　algorithms　are　compared.　The　comparison　results　indicate　that　the　proposed　method　gains　better　enhancement　effects　for　underwater　images　in　different　scenes　than　the　other　enhancement　algorithms,　since　it　can　significantly　diminish　the　color　deviation,　blur,　and　low　contrast　in　degraded　underwater　images.