Mixed X-Ray Image Separation for Artworks with Concealed Designs
This addresses a specific challenge in art conservation and analysis by enabling non-invasive separation of X-ray images without labeled data, though it appears incremental as it builds on existing techniques like algorithm unrolling.
The paper tackles the problem of separating mixed X-ray images of paintings with concealed designs into components for the surface and concealed features, proposing a self-supervised deep learning method and demonstrating it on a real painting by Goya.
In this paper, we focus on X-ray images 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 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 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, Doña Isabel de Porcel by Francisco de Goya, to show its effectiveness.