Scatigno Claudia, Festa Giulia
CREF-Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Via Panisperna 89a, 00189 Rome, Italy.
J Imaging. 2022 Oct 14;8(10):284. doi: 10.3390/jimaging8100284.
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 on cultural heritage domain, but future applications could help to solve challenges of this research field. Here, 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 is reported, spanning from biomedicine, microbiology, and materials science to give new perspectives on future cultural heritage applications.
最近，诸如卷积神经网络之类的学习算法已成功应用于成像环境中从数据采集到数据分析的数据处理的不同阶段。这些算法的目的是降低数据维度和计算量，找到基准并提取特征，以提高成像数据的分辨率和可重复性。目前，尚未将结合学习算法的中子成像应用于文化遗产领域，但未来的应用可能有助于解决该研究领域的挑战。在此，报告了对利用机器学习和深度学习模型应用于X射线成像和中子成像数据处理的开创性工作的综述，涵盖生物医学、微生物学和材料科学，为未来文化遗产应用提供新的视角。
