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Neutron Imaging and Learning Algorithms: New Perspectives in Cultural Heritage Applications

Recently, learning algorithms such as Convolutional Neural Networks have been successfully applied in different stages of data processing from the acquisition to the data analysis in the imaging context. The aim of these algorithms is the dimensionality of data reduction and the computational effort, to find benchmarks and extract features, to improve the resolution, and reproducibility performances of the imaging data. Currently, no Neutron Imaging combined with learning algorithms was applied to the cultural heritage domain, but future applications could help to solve the challenges of this research field.

In this paper, researchers Giulia Festa and Claudia Scatigno from Physics for Heritage Laboratory at Centro Ricerche Enrico Fermi, report a review of pioneering works to exploit the use of Machine Learning and Deep Learning models applied to X-ray imaging and Neutron Imaging data processing, spanning from biomedicine, microbiology, and materials science to give new perspectives on future cultural heritage applications.

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