  Imaging is critical to the characterisation of materials. However, even with
careful sample preparation and microscope calibration, imaging techniques are
often prone to defects and unwanted artefacts. This is particularly problematic
for applications where the micrograph is to be used for simulation or feature
analysis, as defects are likely to lead to inaccurate results. Microstructural
inpainting is a method to alleviate this problem by replacing occluded regions
with synthetic microstructure with matching boundaries. In this paper we
introduce two methods that use generative adversarial networks to generate
contiguous inpainted regions of arbitrary shape and size by learning the
microstructural distribution from the unoccluded data. We find that one
benefits from high speed and simplicity, whilst the other gives smoother
boundaries at the inpainting border. We also outline the development of a
graphical user interface that allows users to utilise these machine learning
methods in a 'no-code' environment.
