Undersampling is a simple but efficient way to increase the imaging rate of atomic force microscopy (AFM). One major challenge in this approach is that of accurate image reconstruction from a limited number of measurements. In this work, we present a deep neural network (DNN) approach to reconstruct $\mu$-path sub-sampled AFM images. Our network consists of two sub-networks, namely a RED-net and a U-net, in series, and is trained end-to-end from random images masked according to $\mu$-path sub-sampling patterns. Using both simulation and experiments, the DNN is shown to yield better image quality than three existing optimization-based methods for reconstruction: basis pursuit, a variant of total variation minimization, and inpainting.

欠采样是提高原子力显微镜（AFM）成像速率的简单但有效的方法。这种方法的一个主要挑战是从有限数量的测量中进行准确的图像重建。在这项工作中，我们提出了一种深度神经网络（DNN）的方法来重建$\mu$路径二次采样的AFM图像。我们的网络由两个子网组成，分别是RED网络和U网络，并通过根据$\mu$-path子采样模式。通过仿真和实验，显示DNN的图像质量比现有的三种基于优化的重建方法更好：基础追踪，最小化总变化量和修复。