Due　to　complex　and　volatile　lighting　environment,　underwater　imaging　can　be　readily　impaired　by　light　scattering,　warping,　and　noises.　To　improve　the　visual　quality,　Underwater　Image　Enhancement　(UIE)　techniques　have　been　widely　studied.　Recent　efforts　have　also　been　contributed　to　evaluate　and　compare　the　UIE　performances　with　subjective　and　objective　methods.　However,　the　subjective　evaluation　is　time-consuming　and　uneconomic　for　all　images,　while　existing　objective　methods　have　limited　capabilities　for　the　newly-developed　UIE　approaches　based　on　deep　learning.　To　fill　this　gap,　we　propose　an　Underwater　Image　Fidelity　(UIF)　metric　for　objective　evaluation　of　enhanced　underwater　images.　By　exploiting　the　statistical　features　of　these　images　in　CIELab　space,　we　present　the　naturalness,　sharpness,　and　structure　indexes.　Among　them,　the　naturalness　and　sharpness　indexes　represent　the　visual　improvements　of　enhanced　images;　the　structure　index　indicates　the　structural　similarity　between　the　underwater　images　before　and　after　UIE.　We　combine　all　indexes　with　a　saliency-based　spatial　pooling　and　thus　obtain　the　final　UIF　metric.　To　evaluate　the　proposed　metric,　we　also　establish　a　first-of-its-kind　large-scale　UIE　database　with　subjective　scores,　namely　Underwater　Image　Enhancement　Database　(UIED).　Experimental　results　confirm　that　the　proposed　UIF　metric　outperforms　a　variety　of　underwater　and　general-purpose　image　quality　metrics.　The　database　and　source　code　are　available　at　https://github.com/z21110008/UIF.