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. In this paper, we deal with the problem of separating mixed X-ray images originating from the radiography of double-sided paintings. 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. In this proposed architecture, the convolutional auto encoders extract features from the RGB images. These features are then used to (1) reproduce both of the original RGB images, (2) reconstruct the hypothetical separated X-ray images, and (3) regenerate the mixed X-ray image. The 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 \textsl{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.

Almost two years ago, an unusual case shook the art world. Art theft and forgery are not uncommon but claiming an exhibited art piece to be "Fake" definitely induce disorder. The issue arose when in a German public collection, a painting thought to be made by artist Kazimir Malevich in 1915 was labeled as counterfeit and a judicial trial was held to seek the truth. Some witnessed and art experts said that it was original enough and "could hang in Stedelijk" while some stated to the authorities that such works were terribly "awful imitations". A similar case in 2018, led to the closure of the Ghent Museum of Fine Art in Belgium after some of the artwork raised suspicion among the critics and as a result, the director of the museum was suspended.

Researchers have harnessed the power of artificial intelligence to decode x-ray images of the Ghent Altarpiece, the 15th-century masterpiece by brothers Hubert van Eyck and Jan van Eyck at the St. Bavo Cathedral in Belgium. Being able to read the x-rays can help identify damage to the painting by showing areas where varnish or overpainting hides cracks, paint loss, or other structural issues. The scans can also teach researchers about the artists' working methods, revealing the physical structure of the canvas or panel and its supports, as well as the different layers of paint used in its creation. But because the Ghent Altarpiece's panels are double sided, it has been difficult to parse the x-ray images. A newly developed algorithm has allowed scientists to deconstruct the data to create two distinct images.

Art and conservation experts regularly rely on X-ray imaging to find out what's going on underneath the surface of a painting. It allows them to learn more about the paint used, the technique, or the painted surface. Now, researchers have found a way to improve this technology with artificial intelligence. Some of the X-ray images of this work have now been further analysed using artificial intelligence. Even though X-ray imaging is an effective way to study the hidden layers of a painting, it doesn't discriminate between the actual layer of interest and everything else.

What's more, they didn't really expect the results of their work to be quite so good. X-ray images are already a valuable tool in the examination and restoration of paintings, as they can reveal the underlying condition of the work and provide insights into artists' techniques. They can also help experts to authenticate works. But there is a problem. Interpreting X-rays can be difficult because the images capture everything – the visible top layer, what is underneath, the materials and support structures such as struts, and anything that is on the back.

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. In the art investigation domain, increasing use of extremely high-resolution digital imaging techniques is being made in parallel with the widespread adoption of a range of recent imaging and analytical modalities not previously applied in the field (e.g., hyperspectral imaging, macro x-ray fluorescence scanning, and novel forms of imaging x-ray radiography) (1–3). These techniques mean that there is a wealth of digital data available within the sector, offering huge scope to provide new insights but also presenting new computational challenges to the domain (4). In the past decades, various other disciplines, experiencing similar data growth, have benefited greatly from recent breakthroughs in artificial intelligence.

Artificial intelligence has been used to analyse high-resolution digital X-ray images of the world famous Ghent Altarpiece, as part of an investigative project led by UCL. The finding is expected to improve our understanding of art masterpieces and provide new opportunities for art investigation, conservation and presentation. Researchers from the National Gallery, Duke University and UCL worked with technical images acquired from the brothers Van Eyck's Ghent Altarpiece, a large and complex 15th-century altarpiece in St Bavo's Cathedral, Belgium. The paper, 'Artificial Intelligence for Art Investigation: Meeting the Challenge of Separating X-ray Images of the Ghent Altarpiece', demonstrates how academics used a newly developed algorithm to study mixed X-ray images containing features from the front and back of the painting's double-sided panels, which scientists have deconstructed into two clear images. These images are part of a comprehensive set of high resolution pictures acquired using different imaging techniques as part of the altarpiece's on-going conservation by the Royal Institute for Cultural Heritage (KIK-IRPA), providing a wealth of data to interrogate and interpret.

Mathematics is everywhere, if you know where to look. A recently opened exhibition at the North Carolina Museum of Art (NCMA) is displaying the St. John Altarpiece, a 14th-century work by Francescuccio Ghissi. It has nine scenes in total: eight smaller pictures featuring St. John the Evangelist flanking a larger central Crucifixion. At the end of the 19th century, the altarpiece was separated into parts by a saw and eight of the nine resulting panels were sold to different collectors. One panel, the last of the smaller scenes, was lost.