  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.
