Atomic　force　microscope　(AFM)　is　an　analytical　instrument　which　is　used　to　study　the　surface　structure　and　morphology　of　materials.　The　AFM　can　measure　and　observe　samples　either　in　air　or　liquid　environment.　However,　the　standard　AFM　requires　a　long　time　to　acquire　accurate　images　and　data.　In　our　work,　the　compressive　sensing　(CS)　was　applied　in　order　to　reduce　the　imaging　time,　lower　the　interactions　between　the　probe　and　the　sample,　finally　avoid　sample　damage　in　AFM.　Three　samples　(PAA　film,　TGG1　grating　and　BOPP　film)　were　used　as　the　testing　samples.　Different　image　reconstruction　algorithms　(l1-ls,　TVAL3,　GPSR　and　IHT)　were　employed　to　reconstruct　AFM　image　with　different　sampling　rate.　And　various　sampling　patterns　(Random　Scan,　Row　Scan,　SRM,　Spiral　Scan　and　Square-shape　Scan)　were　used　to　obtain　the　undersampling　data.　A　large　number　of　experiments　show　that　the　choice　of　sampling　pattern　and　image　reconstruction　algorithm　has　significant　impact　on　the　quality　of　the　reconstructed　images　in　AFM.　Subsequently　the　reconstruction　results　of　sample　topographic　images　were　analyzed　and　evaluated　by　the　image　quality　indicators　(PSNR　and　SSIM).　The　CS　method　can　be　used　to　obtain　accurate　images　by　reducing　measurement　data.　It　finally　improves　the　measurement　speed　of　AFM　without　cutting　down　the　quality　of　AFM　image.　©　2018　Elsevier　B.V.