Microstructure reconstruction via artificial neural networks: A combination of causal and non-causal approach
This work addresses microstructure reconstruction for materials science, but it appears incremental as it builds on existing ANN methods with a hybrid approach.
The authors tackled the problem of reconstructing sponge-like microstructure images using artificial neural networks (ANNs), combining causal prediction with non-causal smoothing, and achieved results quantified by discrepancies in spatial statistics between reference and reconstructed samples.
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. number of densely connected layers, 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.