Machine learning (ML) methods are extraordinarily successful at denoising
photographic images. The application of such denoising methods to scientific
images is, however, often complicated by the difficulty in experimentally
obtaining a suitable expected result as an input to training the ML network.
Here, we propose and demonstrate a simulation-based approach to address this
challenge for denoising atomic-scale scanning tunneling microscopy (STM)
images, which consists of training a convolutional neural network on STM images
simulated based on a tight-binding electronic structure model. As model
materials, we consider graphite and its mono- and few-layer counterpart,
graphene. With the goal of applying it to any experimental STM image obtained
on graphitic systems, the network was trained on a set of simulated images with
varying characteristics such as tip height, sample bias, atomic-scale defects,
and non-linear background. Denoising of both simulated and experimental images
with this approach is compared to that of commonly-used filters, revealing a
superior outcome of the ML method in the removal of noise as well as scanning
artifacts - including on features not simulated in the training set. An
extension to larger STM images is further discussed, along with intrinsic
limitations arising from training set biases that discourage application to
fundamentally unknown surface features. The approach demonstrated here provides
an effective way to remove noise and artifacts from typical STM images,
yielding the basis for further feature discernment and automated processing.Comment: Includes S