Image reconstruction for sub-sampled atomic force microscopy images using deep neural networks

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 μ-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 μ-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.