In　recent　years,　single-image　super-resolution　(SR)　reconstruction　has　aroused　wide　attention.　Massive　SR　enhancement　algorithms　have　been　proposed.　However,　much　less　work　has　been　down　on　the　perceptual　evaluation　of　SR　enhanced　images　and　the　corresponding　enhancement　algorithms.　In　this　work,　we　create　a　Super-resolution　Reconstructed　Image　Database　(SRID),　which　consists　of　images　produced　by　two　interpolation　methods　and　six　popular　SR　image　enhancement　algorithms　at　different　amplification　factors.　Then,　subjective　experiment　is　conducted　to　collect　the　subjective　scores　by　using　the　single-stimulus　method.　The　performances　of　the　SR　image　enhancement　algorithms　are　then　evaluated　by　the　obtained　subjective　scores.　Finally,　the　performances　of　the　general-purpose　no-reference　(NR)　image　quality　metrics　are　investigated　on　the　SRID　database.　This　study　shows　that　it　is　difficult　for　the　state-of-the-art　NR　image　quality　metrics　to　predict　the　quality　of　SR　enhanced　images.　©　2017　IEEE.