Atomic　force　microscope　(AFM)　is　a　powerful　nanoscale　instrument,　which　can　obtain　the　true　surface　morphology　of　samples.　There　are　strict　requirements　for　the　detailed　features　in　AFM　images,　so　it　is　necessary　to　mine　the　deep　information　in　the　images.　Nevertheless,　the　standard　AFM　scanning　process　takes　a　very　long　time　to　obtain　high-quality　images.　For　most　cases,　the　original　images　of　AFM　are　with　low　resolution.　In　order　to　get　the　more　detailed　texture　and　feature　information　as　much　as　possible,　a　super-resolution　convolutional　neural　network　algorithm　based　on　enhanced　data　set　is　proposed　in　AFM　imaging.　By　learning　the　mapping　relationship　between　low-resolution　images　and　high-resolution　images　from　the　image　database,　the　high-resolution　image　is　finally　obtained.　Aiming　at　the　problem　of　long　scanning　time　and　too　small　training　database　of　AFM　image,　adaptive　histogram　equalization　is　used　to　expand　and　enhance　the　training　set　of　AFM　images.　Compared　with　the　traditional　super-resolution　methods,　the　subjective　and　objective　evaluation　of　the　reconstructed　image　verifies　the　feasibility　of　the　proposed　algorithm.　Aiming　at　the　problem　of　long　scanning　time　and　too　small　training　database　of　AFM　image,　a　super-resolution　convolutional　neural　network　algorithm　based　on　enhanced　data　set　is　proposed　in　AFM　imaging.　It　can　save　a　lot　of　time　and　hardware　cost　by　using　adaptive　histogram　equalization　(AHE)　and　improve　the　generalization　ability　of　deep　learning　model　to　further　improve　the　reconstruction　quality　of　AFM　image.