Neural network simulation of original colors in Friedrich’s Abbey Among Oak Trees featuring discoloured smalt
Clément de Mecquenem (PSL University, ParisTech, Institut Photonique d'Analyse Non-destructive Européenne des Matériaux du Anciens, French Ministry of Culture, Université de Versailles St-Quentin)
Myriam Eveno (ParisTech, National Centre for Research and Restoration in French Museums, Institut de Recherche de Chimie Paris, PSL University)
Matthias Alfeld (TU Delft - Team Matthias Alfeld)
Thomas Calligaro (French Ministry of Culture, ParisTech, Institut Photonique d'Analyse Non-destructive Européenne des Matériaux du Anciens, Université de Versailles St-Quentin, PSL University)
Eric Laval (French Ministry of Culture, National Centre for Research and Restoration in French Museums)
Kristina Mösl (Staatliche Museen zu Berlin-Stiftung Preussischer Kulturbesitz)
Ina Reiche (PSL University, French Ministry of Culture, ParisTech)
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Abstract
Artwork appearances change over time due to aging. Smalt, a blue cobalt-tinted glass pigment, deteriorates over time in oil paintings causing significant and irreversible color changes in many artworks. Virtual simulations can hypothesis original appearances while it remains a challenge for smalt-containing paintings. A novel procedure integrates non-invasive imaging methods, X-ray absorption near-edge structure (XANES), and machine learning to simulate the original colors of a smalt-containing discolored paintings. Macro-X-ray fluorescence provided elemental distribution, reflectance imaging spectroscopy captured color spectra of pigments and XANES informed cobalt speciation in cross sections. Friedrich’s Abbey Among Oak Trees (1808-1810) containing smalt and artificially aged model systems were studied. Machine learning predicted the original hues based on XANES. The procedure allowed us to simulate the original, cooler and more vibrant colors of the painting. The innovative approach visualizes a possible original state of the smalt-containing artwork that can be adapted to other alteration phenomena.
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