Artefact removal from micrographs with deep learning based inpainting

Imaging is critical to the characterisation of materials. However, even with careful sample preparation and microscope calibration, imaging techniques can contain defects and unwanted artefacts. This is particularly problematic for applications where the micrograph is to be used for simulation or feature analysis, as artefacts are likely to lead to inaccurate results. Microstructural inpainting is a method to alleviate this problem by replacing artefacts 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 describe an open-access graphical user interface that allows users to utilise these machine learning methods in a 'no-code' environment. We present a novel inpainting algorithm for microstructural image data using generative adversarial networks. This enables fast artefact removal via a simple graphical user interface.