Mixed X-Ray Image Separation for Artworks with Concealed Designs

TL;DR

This paper introduces a self-supervised deep learning method for separating X-ray images of artworks into visible and concealed layers, enabling better analysis of hidden features without needing paired training data.

Contribution

It proposes a novel self-supervised deep learning approach using analysis and synthesis networks with algorithm unrolling for X-ray image separation in artworks.

Findings

Effective separation demonstrated on Goya's painting with concealed content.

No need for paired mixed and separated images during training.

Improves analysis of hidden features in artworks.

Abstract

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, Do\~na Isabel de Porcel by Francisco de Goya, to show its effectiveness.