Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the Ghent Altarpiece

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.