High-quality element distribution maps enable pre-cise analysis of Old Master paintings. These maps are typically produced by Macro X-ray Fluorescence (MA-XRF) scanning, a non-invasive technique for elemental imaging of flat surfaces. However, MA-XRF faces a trade-off between resolution and acquisition time, making high-resolution (HR) scans impractical for large artworks. Super-resolution MA-XRF mitigates this by enhancing scan quality while reducing acquisition time. This paper introduces a deep learning framework for MA-XRF super-resolution that removes the need for paired HR MA-XRF training data by leveraging RGB images to model cross-modal dependencies. Our approach is specifically tailored for MA-XRF, an important feature as RGB and MA-XRF data lack a common spectral domain. We introduce self-supervised adversarial training, where the discriminator learns from patches across modalities, guiding the generator toward realistic MA-XRF reconstructions. Additionally, our method enforces physical consistency via network design and enhances training through pseudo-real data augmentation. Experiments on Old Master paintings show our method outperforms state-of-the-art MA-XRF super-resolution techniques, demonstrating the need for tailored solutions as existing approaches from other domains do not generalize effectively to this task.

During the conservation of easel paintings, black draperies or dark backgrounds often prove problematic to treat and understand in terms of condition. Despite most black pigments in Old Master paintings not intrinsically containing high atomic number elements, the investigation of black paints is one of the National Gallery's major uses and most helpful applications of x-ray fluorescence (XRF) scanning. As a visual tool, XRF element distribution maps of black paints are useful for assessing the extent of overpainting or condition of any surviving original paint beneath, and for revealing the original modelling. However, interpreting the XRF element maps can be challenging. It is important to consider not only the primary compositions of black or dark pigments, but to have an understanding of other possible associated materials and more broadly of historical painting techniques, artists' practices and the paint stratigraphy. Through a series of case studies, this paper demonstrates how a detailed knowledge of historic painting materials and techniques, based on study of documentary sources and archives of materials-based evidence from the analysis of paint cross-sections-both from the paintings being studied and related works-aids and enriches interpretation of XRF element maps of black paints. While samples are invaluable in understanding exactly how-and with what materials-such regions were painted, they are not always representative and scanning XRF may be of complementary benefit in providing a more holistic view of pigment distribution. The examples presented range from the fifteenth to the seventeenth century from both Northern Europe and Italy.

High-quality element distribution maps enable precise analysis of the material composition and condition of Old Master paintings. These maps are typically produced from data acquired through Macro X-ray fluorescence (MA-XRF) scanning, a non-invasive technique that collects spectral information. However, MA-XRF is often limited by a trade-off between acquisition time and resolution. Achieving higher resolution requires longer scanning times, which can be impractical for detailed analysis of large artworks. Super-resolution MA-XRF provides an alternative solution by enhancing the quality of MA-XRF scans while reducing the need for extended scanning sessions. This paper introduces a tailored super-resolution approach to improve MA-XRF analysis of Old Master paintings. Our method proposes a novel adversarial neural network architecture for MA-XRF, inspired by the Learned Iterative Shrinkage-Thresholding Algorithm. It is specifically designed to work in an unsupervised manner, making efficient use of the limited available data. This design avoids the need for extensive datasets or pre-trained networks, allowing it to be trained using just a single high-resolution RGB image alongside low-resolution MA-XRF data. Numerical results demonstrate that our method outperforms existing state-of-the-art super-resolution techniques for MA-XRF scans of Old Master paintings.

The anniversary in 2019 of Leonardo’s death prompted a new investigation of the Virgin of the Rocks now in the National Gallery in London. This built on a substantial foundation of earlier research, one past milestone being the discovery through infrared reflectography of an underdrawing for a completely different composition, showing a Virgin higher up on the panel and in a different pose. This paper focuses on the latest findings associated with this first abandoned composition, discussing how the re-examination using macro X-ray fluorescence (XRF) scanning and reflectance imaging spectroscopy (RIS) in the infrared range allowed a much more complete visualisation of the initial design, found to include figures for the Christ Child and an angel. The new study also provided interesting insights into Leonardo’s underdrawing materials and methods, identifying different drawing media (which include zinc-containing iron gall inks mixed with variable amounts of carbon black), seemingly used at different stages of the creative process to develop initial ideas into a more defined composition. The findings are relevant to the long genesis of the painting and its relationship with the version in the Musée du Louvre, Paris.

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.

Macro X-ray fluorescence (MA-XRF) scanning is commonly used to non-invasively analyse Old Master paintings by mapping the distribution of the chemical elements present in the artworks. The visual quality of the element distribution maps is very important for characterising the materials and understanding the execution and condition of the painting. However, this quality is limited by the acquisition time for the XRF datacube, resulting in a trade-off between signal-to-noise ratio (SNR) and spatial resolution. To solve this problem we propose to enhance the spatial resolution of the XRF datacube of a painting leveraging a corresponding high-resolution (HR) RGB image. We achieve that by introducing a method based on coupled dictionary learning along with a similarity constraint based on mutual information. In particular, we divide the RGB image and the XRF datacube into a common part and a unique part based on whether the information is shared or not, and then transfer the HR information between the two common parts, resulting in high-quality reconstructions. Numerical results show that our XRF super-resolution method outperforms the other state-of-the-art approaches.

We present an automated method for registration and mosaicking of multimodal technical images of artworks based on mutual information. We focus on the registration of element distribution maps resulting from macro X-ray fluorescence (MA-XRF) scanning, which can be considered as a layered stack and treated as the moving image. The target fixed image is the visible image of the same artwork. In consecutive stages, a unique, optimised transformation that provides the highest average mutual information across all images in the stack is identified with consensus. This transformation can be applied to the moving image to obtain the best alignment between the moving and fixed images when overlapped.

X-radiography (X-ray imaging) is a widely used imaging technique in art investigation. It can provide information about the condition of a painting as well as insights into an artist’s techniques and working methods, often revealing hidden information invisible to the naked eye. X-radiograpy of double-sided paintings results in a mixed X-ray image and this paper deals with the problem of separating this mixed image. Using the visible color images (RGB images) from each side of the painting, we propose a new Neural Network architecture, based upon ‘connected’ auto-encoders, designed to separate the mixed X-ray image into two simulated X-ray images corresponding to each side. This connected auto-encoders architecture is such that the encoders are based on convolutional learned iterative shrinkage thresholding algorithms (CLISTA) designed using algorithm unrolling techniques, whereas the decoders consist of simple linear convolutional layers; the encoders extract sparse codes from the visible image of the front and rear paintings and mixed X-ray image, whereas the decoders reproduce both the original RGB images and the mixed X-ray image. 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 methodology was tested on images from the double-sided wing panels of the Ghent Altarpiece, painted in 1432 by the brothers Hubert and Jan van Eyck. These tests show that the proposed approach outperforms other state-of-the-art X-ray image separation methods for art investigation applications.

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.

In recent decades, cultural heritage research—and in particular art investigation—has been undergoing a digital revolution. This is due both to improvements in the digitization and the acquisition of artifact’s images generated using traditional 2-D imaging methods as well as the growing adoption of a range of more recently introduced spectroscopic imaging techniques. A number of these imaging modalities use wavelengths of electromagnetic radiation that can penetrate surface layers thus yielding information from hidden features noninvasively. Different techniques are often used in combination to provide evidence of construction, condition, and past treatment. These can also be used to characterize the materials used, how they were combined, and map their distribution, giving insight into an artist’s working method and the means to understand changes that have occurred over time. This wealth of data calls for the development of algorithmic approaches in order to handle and fully explore and interpret it. The questions one seeks to answer are in some cases sufficiently different from those addressed in other fields that no existing off-the-shelf approaches can be applied. In this article, we discuss a few of the algorithmic challenges that arise in art investigation and conservation using modern imaging techniques.

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.

Undertaking the conservation of artworks informed by the results of molecular analyses has gained growing importance over the last decades, and today it can take advantage of state-of-the-art analytical techniques, such as mass spectrometry-based proteomics. Protein-based binders are among the most common organic materials used in artworks, having been used in their production for centuries. However, the applications of proteomics to these materials are still limited. In this work, a palaeoproteomic workflow was successfully tested on paint reconstructions, and subsequently applied to micro-samples from a 15th-century panel painting, attributed to the workshop of Sandro Botticelli. This method allowed the confident identification of the protein-based binders and their biological origin, as well as the discrimination of the binder used in the ground and paint layers of the painting. These results show that the approach is accurate, highly sensitive, and broadly applicable in the cultural heritage field, due to the limited amount of starting material required. Accordingly, a set of guidelines are suggested, covering the main steps of the data analysis and interpretation of protein sequencing results, optimised for artworks.

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.

X-ray images are widely used in the study of paintings. When a painting has hidden sub-surface features (e.g., reuse of the canvas or revision of a composition by the artist), the resulting X-ray images can be hard to interpret as they include contributions from both the surface painting and the hidden design. In this paper we propose a self-supervised deep learning-based image separation approach that can be applied to the X-ray images from such paintings (‘mixed X-ray images’) to separate them into two hypothetical X-ray images, one containing information related to the visible painting only and the other containing the hidden features. The proposed approach involves two steps: (1) separation of the mixed X-ray image into two images, guided by the combined use of a reconstruction and an exclusion loss; (2) even allocation of the error map into the two individual, separated X-ray images, yielding separation results that have an appearance that is more familiar in relation to Xray images. The proposed method was demonstrated on a real painting with hidden content, Doña Isabel de Porcel by Francisco de Goya, to show its effectiveness.

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.

Recent imaging, examination, and analysis of the few surviving fragments of wall painting from St Stephen’s Chapel have revealed new data relating to the original technique and aspects of workshop practice in the production of these important mid-fourteenth-century wall paintings. Infrared imaging of the paintings provides clear evidence for the presence of an under-drawing and of extensive modification of the design in situ at an advanced stage of the painting process. There are marked differences in the character of the under-drawing on the various fragments studied, which are likely to relate to different hands and may be indicative of workshop practice. In addition, the presence of an original varnish is strongly suggested, the red dyestuff employed for the red lake pigment has been identified, and the complexity of pigment mixtures and stratigraphy of the paint layers has also been elucidated. This information will be discussed in the context of the documentary sources and of analytical results from the investigation of contemporaneous polychromy.

Catherine Higgitta*, Lynne Harrisona, Tomasz Galikowskib, Mike Paub & Peter Hensonba National Gallery, London, UKb Bickerdike Allen Partners, London, UK

Macro X-Ray Fluorescence (XRF) scanning is an increasingly widely used imaging technique for the non-invasive detection and mapping of chemical elements in Old Master paintings. Existing approaches for XRF signal analysis require varying degrees of expert user input. They are mainly based on peak fitting at fixed energies associated with each element and require the target elements to be selected manually. In this paper, we propose a new method that can process macro XRF scanning data from paintings fully automatically. The method consists of two parts: 1) detecting pulses in an XRF spectrum using Finite Rate of Innovation (FRI) theory; 2) producing the distribution maps for each element automatically identified in the painting. The results presented show the ability of our method to detect weak or partially overlapping signals and more excitingly to have visualisation of underdrawing in a masterpiece by Leonardo da Vinci.

X-radiography is a widely used imaging technique in art investigation, whether to investigate the condition of a painting or provide insights into artists' techniques and working methods. In this paper, we propose a new architecture based on the use of `connected' auto-encoders in order to separate mixed X-ray images acquired from double-sided paintings, where in addition to the mixed X-ray image one can also exploit the two RGB images associated with the front and back of the painting. This proposed architecture uses convolutional auto-encoders that extract features from the RGB images that can be employed to (1) reproduce both of the original RGB images, (2) reconstruct the associated separated X-ray images, and (3) regenerate the mixed X-ray image. It operates in a totally self-supervised fashion without the need for examples containing both the mixed X-ray images and the separated ones. Based on images from the double-sided wing panels from the famous Ghent Altarpiece, painted in 1432 by the brothers Hubert and Jan Van Eyck, the proposed algorithm has been experimentally verified to outperform state-of-the-art X-ray separation methods in art investigation applications.

Vibration generated by use of masonry equipment can be a threat to wall paintings. Its assessment, mitigation and control is of critical importance to risk management and the safeguarding of works of art, particularly those which are immovable. The planned programme of window restoration in the Royal Gallery, Palace of Westminster, started in 2016, directly above the monumental wall painting of Trafalgar, had the potential to cause serious damage to this painting. For a period of 18 months the Curator's Office worked with stakeholders, conservation scientists, conservators and stone masons to minimise risk. The project developed a monitoring strategy, set vibration thresholds, managed risk and negotiated the use of tools and working methods. To assess vibration data loggers equipped with 3-axis acceleration sensors were installed at critical locations. To corroborate these findings assessment was undertaken using a sound level analyser. This assessed acceleration at a series of frequencies in the 0-100 Hz range considered most damaging to artworks. However, a critical component was conservator-led in situ monitoring of vibration. When necessary, this live monitoring allowed conservators to dynamically assess and negotiate the adaptation of tools and techniques to reduce levels. This real-time approach proved essential in understanding peaks and changes in levels of vibration; informing discussion between stakeholders, ensuring monitoring translated into effective preservation.