2021/10/19 by Kryštof Latka, Latka, Kryštof, Martin Doškář +3
Computer Science · Engineering · Materials Science · Physics and Astronomy · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Physical sciences #Industrial Vision Systems and Defect Detection #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Neural Networks and Applications #cond-mat.mtrl-sci #cs.CV
paper · pdf · doi:10.48550/arxiv.2110.09815
6 pages, 4 figures, and 7 tables
arxiv created 2021/10/19 · openalex publication_date 2021/10/19 · arxiv updated 2021/10/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.