X-ray fluorescence (XRF) spectroscopy is an analytical technique used to identify chemical elements that has found widespread use in the cultural heritage sector to characterise artists' materials including the pigments in paintings. It generates a spectrum with characteristic emission lines relating to the elements present, which is interpreted by an expert to understand the materials therein. Convolutional neural networks (CNNs) are an effective method for automating such classification tasks—an increasingly important feature as XRF datasets continue to grow in size—but they require large libraries that capture the natural variation of each class for training. As an alternative to having to acquire such a large library of XRF spectra of artists' materials a physical model, the Fundamental Parameters (FP) method, was used to generate a synthetic dataset of XRF spectra representative of pigments typically encountered in Renaissance paintings that could then be used to train a neural network. The synthetic spectra generated—modelled as single layers of individual pigments—had characteristic element lines closely matching those found in real XRF spectra. However, as the method did not incorporate effects from the X-ray source, the synthetic spectra lacked the continuum and Rayleigh and Compton scatter peaks. Nevertheless, the network trained on the synthetic dataset achieved 100% accuracy when tested on synthetic XRF data. Whilst this initial network only attained 55% accuracy when tested on real XRF spectra obtained from reference samples, applying transfer learning using a small quantity of such real XRF spectra increased the accuracy to 96%. Due to these promising results, the network was also tested on select data acquired during macro XRF (MA-XRF) scanning of a painting to challenge the model with noisier spectra Although only tested on spectra from relatively simple paint passages, the results obtained suggest that the FP method can be used to create accurate synthetic XRF spectra of individual artists' pigments, free from X-ray tube effects, on which a classification model could be trained for application to real XRF data and that the method has potential to be extended to deal with more complex paint mixtures and stratigraphies.

X-ray images of polyptych wings, or other artworks painted on both sides of their support, contain in one image content from both paintings, making them difficult for experts to “read.” To improve the utility of these x-ray images in studying these artworks, it is desirable to separate the content into two images, each pertaining to only one side. This is a difficult task for which previous approaches have been only partially successful. Deep neural network algorithms have recently achieved remarkable progress in a wide range of image analysis and other challenging tasks. We, therefore, propose a new self-supervised approach to this x-ray separation, leveraging an available convolutional neural network architecture; results obtained for details from the Adam and Eve panels of the Ghent Altarpiece spectacularly improve on previous attempts.

The fugitive nature of the colorants obtained from sappanwood (Caesalpinia sappan L.) or the South American species commonly known as ‘brazilwoods’ (including other Caesalpinia species and Paubrasilia echinata (Lam.)) makes the identification of brazilwood dyes and pigments in historic artefacts analytically challenging. This difficulty has been somewhat alleviated recently by the recognition and structural elucidation of a relatively stable marker component found in certain brazilwood dyes and pigments—the benzochromenone metabolite urolithin C. This new understanding creates an ideal opportunity to explore the possibilities for urolithin C’s localization and identification in historical artefacts using a variety of analytical approaches. Specifically, in this work, micro-destructive surface-enhanced Raman spectroscopic methods following a one-sample two-step (direct application of the colloid and then subsequent exposure of the same sample to HF before reapplication of the colloid) approach are utilized for the examination of four historical brazilwood dyed textiles with the results confirmed via HPLC-DAD analysis. It is shown that characterization of reference urolithin C is possible, and diagnostic features of this molecule can also be traced in faded historical linen, silk and wool textiles, even in the presence of minor quantities of flavonoid, indigoid and tannin components. The exploitation of the same micro-sample through a series of SERS analyses affords a fuller potential for confirming the characterization of this species.

Macro X-ray Fluorescence (MA-XRF) scanning is an increasingly widely used technique for analytical imaging of paintings and other artworks. The datasets acquired must be processed to produce maps showing the distribution of the chemical elements that are present in the painting. Existing approaches require varying degrees of expert user intervention, in particular to select a list of target elements against which to fit the data. In this paper, we propose a novel approach that can automatically extract and identify chemical elements and their distributions from MA-XRF datasets. The proposed approach consists of three parts: 1) pre-processing steps, 2) pulse detection and model order selection based on Finite Rate of Innovation theory, and 3) chemical element estimation based on Cramér-Rao bounding techniques. The performance of our approach is assessed using MA-XRF datasets acquired from paintings in the collection of the National Gallery, London. The results presented show the ability of our approach to detect elements with weak X-ray fluorescence intensity and from noisy XRF spectra, to separate overlapping elemental signals and, excitingly, to aid visualisation of hidden underdrawing in a masterpiece by Leonardo da Vinci.

In this paper, we focus on X-ray images (X-radiographs) of paintings with concealed sub-surface designs ( e.g. , deriving from reuse of the painting support or revision of a composition by the artist), which therefore include contributions from both the surface painting and the concealed features. In particular, we propose a self-supervised deep learning-based image separation approach that can be applied to the X-ray images from such paintings to separate them into two hypothetical X-ray images. One of these reconstructed images is related to the X-ray image of the concealed painting, while the second one contains only information related to the X-ray image of the visible painting. The proposed separation network consists of two components: the analysis and the synthesis sub-networks. The analysis sub-network is based on learned coupled iterative shrinkage thresholding algorithms (LCISTA) designed using algorithm unrolling techniques, and the synthesis sub-network consists of several linear mappings. The learning algorithm operates in a totally self-supervised fashion without requiring a sample set that contains both the mixed X-ray images and the separated ones. The proposed method is demonstrated on a real painting with concealed content, Do na Isabel de Porcel by Francisco de Goya, to show its effectiveness.

Macro X-ray Fluorescence (MA-XRF) scanning is increasingly widely used by researchers in heritage science to analyse easel paintings as one of a suite of non-invasive imaging techniques. The task of processing the resulting MA-XRF datacube generated in order to produce individual chemical element maps is called MA-XRF deconvolution. While there are several existing methods that have been proposed for MA-XRF deconvolution, they require a degree of manual intervention from the user that can affect the final results. The state-of-the-art AFRID approach can automatically deconvolute the datacube without user input, but it has a long processing time and does not exploit spatial dependency. In this paper, we propose two versions of a fast automatic deconvolution (FAD) method for MA-XRF datacubes collected from easel paintings with ADMM (alternating direction method of multipliers) and FISTA (fast iterative shrinkage-thresholding algorithm). The proposed FAD method not only automatically analyses the datacube and produces element distribution maps of high-quality with spatial dependency considered, but also significantly reduces the running time. The results generated on the MA-XRF datacubes collected from two easel paintings from the National Gallery, London, verify the performance of the proposed FAD method.