Atomic force microscopy (AFM) provides unprecedented insight into surface topography research with ultrahigh spatial resolution at the subnanometer level. However, a slow scanning rate has to be employed to ensure the image quality, which will largely increase the accumulated sample drift, thereby, resulting in the low fidelity of the AFM image. In this paper, we propose a fast imaging method which performs a complete fast Raster scanning and a slow μ-path subsampling together with a deep learning algorithm to rapidly produce an AFM image with high quality and small drift. A supervised convolutional neural network (CNN) model is trained with the slow μ-path subsampled data and its counterpart acquired with fast Raster scan. The fast speed acquired AFM image is then inputted to the well-trained CNN model to output the high quality one. We validate the reliability of this method using a silicon grids sample and further apply it to the fast imaging of a vanadium dioxide thin film. The results demonstrate that this method can largely improve the imaging speed up to 10.3 times with state-of-the-art imaging quality, and reduce the sample drift by 8.9 times in the multiframe AFM imaging of the same area. Furthermore, we prove that this method is also applicable to other scanning imaging techniques such as scanning electrochemical microscopy.