In this section
@article{Squires:2023:10.1039/d2dd00120a,
author = {Squires, I and Dahari, A and Cooper, SJ and Kench, S},
doi = {10.1039/d2dd00120a},
journal = {Digital Discovery},
pages = {316--326},
title = {Artefact removal from micrographs with deep learning based inpainting},
url = {http://dx.doi.org/10.1039/d2dd00120a},
volume = {2},
year = {2023}
}
TY - JOUR
AB - 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.
AU - Squires,I
AU - Dahari,A
AU - Cooper,SJ
AU - Kench,S
DO - 10.1039/d2dd00120a
EP - 326
PY - 2023///
SN - 2635-098X
SP - 316
TI - Artefact removal from micrographs with deep learning based inpainting
T2 - Digital Discovery
UR - http://dx.doi.org/10.1039/d2dd00120a
UR - http://dx.doi.org/10https://pubs.rsc.org/en/content/articlelanding/2023/DD/D2DD00120A.1039/d2dd00120a
UR - http://hdl.handle.net/10044/1/104029
VL - 2
ER -
Dyson School of Design Engineering
Imperial College London
25 Exhibition Road
South Kensington
London
SW7 2DB
design.engineering@imperial.ac.uk
Tel: +44 (0) 20 7594 8888