Gestels Arthur, Gabrieli Francesca, De Kerf Thomas, Vanmeert Frederik, García Hernan Fernández, Delaney John, Janssens Koen, Steenackers Gunther, Vanlanduit Steve
University of Antwerp, Department of Physics, AXIS Research Group, Groenenborgerlaan 171, B-2020, Antwerp, Belgium; University of Antwerp, Faculty of Applied Engineering, Department Electromechanics InViLab Research Group, Groenenborgerlaan 171, B-2020, Antwerp, Belgium.
Conservation and Science Department, Rijksmuseum, Hobbemastraat 22, 1017 ZC, Amsterdam, the Netherlands.
Talanta. 2024 Dec 1;280:126731. doi: 10.1016/j.talanta.2024.126731. Epub 2024 Aug 18.
Hyperspectral imaging techniques have emerged as powerful tools for non-invasive investigation of artworks. This paper employs either reflectance imaging spectroscopy (RIS) or macroscopic X-ray fluorescence (MA-XRF) imaging in combination with macroscopic X-ray powder diffraction (MA-XRPD) for state-of-the-art chemical imaging of painted cultural heritage artefacts. While RIS can provide molecular information and MA-XRF can offer elemental distribution maps of paintings of high lateral resolution, the unique advantage of MA-XRPD lies in its ability to visualize the distributions of specific pigments and estimate in a quantitative manner the relative concentrations of the crystalline phases at the surface of artworks. However, MA-XRPD is more time-consuming and offers a lower lateral resolution than RIS and MA-XRF.
This study introduces a machine learning (ML) approach to obtain the distribution of specific compounds on the surface of artworks with a resolution that is comparable to that of RIS and MA-XRF data but with the compound specificity of MA-XRPD. The general aim is to expedite non-destructive artwork imaging analysis by fusing data from different imaging modalities via machine learning models. The effect of preprocessing techniques to enhance the predictive accuracy of the models is explored. The paper demonstrates the method's efficacy on a 16th-century illuminated manuscript, showcasing the feasibility of predicting compound-specific distribution maps. Three evaluation methods-visual examination of the predicted distribution, root mean square errors (RMSE), and feature permutation importance (FPI)-are employed to assess model performance. Fusing MA-XRF with MA-XRPD led to the best RMSE scores overall. However, fusing the RIS and MA-XRPD data blocks also yield very satisfactory and easily interpretable high-resolution compound maps.
While MA-XRPD allows for highly specific imaging of artworks, its time-consuming nature and limited resolution presents a bottleneck during non-invasive imaging of painted works of art. By integrating data from more time-efficient hyperspectral techniques such as MA-XRF and RIS, and employing machine learning, we expedite the process without compromising accuracy. The fusion process can also denoise the distribution maps, improving their readability for heritage professionals and art historical scholars.
高光谱成像技术已成为用于艺术品非侵入性研究的强大工具。本文采用反射成像光谱（RIS）或宏观X射线荧光（MA-XRF）成像，并结合宏观X射线粉末衍射（MA-XRPD），对彩绘文化遗产文物进行先进的化学成像。虽然RIS可以提供分子信息，MA-XRF可以提供具有高横向分辨率的绘画元素分布图，但MA-XRPD的独特优势在于它能够可视化特定颜料的分布，并以定量方式估计艺术品表面结晶相的相对浓度。然而，MA-XRPD比RIS和MA-XRF更耗时，横向分辨率也更低。
本研究引入了一种机器学习（ML）方法，以获得艺术品表面特定化合物的分布，其分辨率与RIS和MA-XRF数据相当，但具有MA-XRPD的化合物特异性。总体目标是通过机器学习模型融合来自不同成像模态的数据，加快艺术品无损成像分析。探索了预处理技术对提高模型预测准确性的影响。本文展示了该方法在一份16世纪的 illuminated manuscript上的有效性，证明了预测化合物特定分布图的可行性。采用了三种评估方法——预测分布的视觉检查、均方根误差（RMSE）和特征排列重要性（FPI）——来评估模型性能。将MA-XRF与MA-XRPD融合总体上获得了最佳的RMSE分数。然而，融合RIS和MA-XRPD数据块也能产生非常令人满意且易于解释的高分辨率化合物图。
虽然MA-XRPD允许对艺术品进行高度特异性成像，但其耗时的特性和有限的分辨率在彩绘艺术品的非侵入性成像过程中构成了瓶颈。通过整合来自MA-XRF和RIS等更高效的高光谱技术的数据，并采用机器学习，我们在不影响准确性的情况下加快了这一过程。融合过程还可以对分布图进行去噪，提高其对遗产专业人员和艺术史学者的可读性。
