Atomic　force　microscopy　(AFM)　is　essential　for　studying　the　surface　properties　of　samples　at　the　micro-　and　nanoscales.　Traditional　AFM　scanning　methods　are　time-consuming,　particularly　for　obtaining　high-resolution　images.　Compressive　sensing　(CS)　has　been　utilized　for　fast　AFM　imaging.　However,　as　the　size　and　resolution　requirements　of　the　images　increase,　the　measurement　matrix　for　compressive　sensing　also　becomes　larger.　Block　compressive　sensing　(BCS)　divides　the　image　into　blocks　and　reconstructs　them　with　a　small　measurement　matrix,　but　it　is　difficult　to　balance　the　imaging　quality　between　regions.　Therefore,　we　propose　an　innovative　adaptive　CS-AFM　imaging　scheme.　A　low-resolution　image　is　obtained　through　fast　scanning,　and　a　high-resolution　image　is　generated　using　bicubic　interpolation.　The　Otsu　and　eight-connectivity　methods　detect　the　location　of　the　target　blocks,　while　the　GRNN　model　adapts　the　sampling　rate　for　it.　A　supplementary　scan　is　performed　on　the　target　block,　followed　by　reconstruction　using　the　TVAL3　algorithm.　Finally,　the　target　region　is　replaced　with　the　reconstructed　high-quality　target　blocks.　Compared　to　other　schemes,　the　results　demonstrate　that　our　method　excels　in　achieving　fast,　high-quality,　and　high-resolution　imaging.　©　2025　Wiley　Periodicals　LLC.