Correction of AFM data artifacts using a convolutional neural network trained with synthetically generated data

• https://www.sciencedirect.com/science/article/pii/S0304399122001851?via%3Dihub

Atomic force microscopy, Reconstruction by CNN, Machine learning for atomic force microscopy, Automatic image correction, Synthetic training data generation

AFM microscopy from its nature produces outputs with certain distortions,
inaccuracies and errors given by its physical principle. These distortions are
more or less well studied and documented. Based on the nature of the individual
distortions, different reconstruction and compensation filters have been
developed to post-process the scanned images. This article presents an approach
based on machine learning - the involved convolutional neural network learns from
pairs of distorted images and the ground truth image and then it is able to
process pairs of images of interest and produce a filtered image with the
artifacts removed or at least suppressed. What is important in our approach is
that the neural network is trained purely on synthetic data generated by
a simulator of the inputs, based on an analytical description of the physical
phenomena causing the distortions. The generator produces training samples
involving various combinations of the distortions. The resulting trained network
seems to be able to autonomously recognize the distortions present in the testing
image (no knowledge of the distortions or any other human knowledge is provided
at the test time) and apply the appropriate corrections. The experimental results
show that not only is the new approach better or at least on par with
conventional post-processing methods, but more importantly, it does not require
any operator's input and works completely autonomously. The source codes of the
training set generator and of the convolutional neural net model are made public,
as well as an evaluation dataset of real captured AFM images.