Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the <i>Ghent Altarpiece</i>.

Abstract

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 <i>Adam</i> and <i>Eve</i> panels of the <i>Ghent Altarpiece</i> spectacularly improve on previous attempts.