Microstructure reconstruction via artificial neural networks: a combination of causal and non-causal approach
Typ dokumentu
articlePeer-reviewed
publishedVersion
Autor
Latka, Kryštof
Doškář, Martin
Zeman, Jan
Práva
Creative Commons Attribution 4.0 International Licensehttp://creativecommons.org/licenses/by/4.0/
openAccess
Metadata
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We investigate the applicability of artificial neural networks (ANNs) in reconstructing a sample image of a sponge-like microstructure. We propose to reconstruct the image by predicting the phase of the current pixel based on its causal neighbourhood, and subsequently, use a non-causal ANN model to smooth out the reconstructed image as a form of post-processing. We also consider the impacts of different configurations of the ANN model (e.g., the number of densely connected layers, the number of neurons in each layer, the size of both the causal and non-causal neighbourhood) on the models’ predictive abilities quantified by the discrepancy between the spatial statistics of the reference and the reconstructed sample.
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