Issue 2, 2023

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.

Graphical abstract: Artefact removal from micrographs with deep learning based inpainting

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Article information

Article type
07 Nov 2022
02 Feb 2023
First published
03 Feb 2023
This article is Open Access
Creative Commons BY license

Digital Discovery, 2023,2, 316-326

Artefact removal from micrographs with deep learning based inpainting

I. Squires, A. Dahari, S. J. Cooper and S. Kench, Digital Discovery, 2023, 2, 316 DOI: 10.1039/D2DD00120A

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