Machine learning for painting conservation: a state-of-the-art review

Aster Van Vijle · Piraye Hacıgüzeller · Geert Van der Snickt

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Cultural Heritage Materials Analysis Conservation Techniques and Studies Image Processing and 3D Reconstruction

Abstract

Abstract This review discusses recent machine learning applications in painting conservation, highlighting five themes: enhancement of scientific imagery, pigment analysis, damage detection, virtual restoration, and damage prediction. A persistent challenge is the scarcity of high-quality historical data, limiting model reliability and scope. While supervised learning remains widespread, unsupervised methods hold promise for revealing nuanced patterns within complex datasets. The review seeks to foster collaboration between machine learning practitioners and conservation professionals.

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2017Hyperspectral imaging combined with data classification techniques as an aid for artwork authentication 2018Automatic pigment identification from hyperspectral data 2015Digital Image Processing of The Ghent Altarpiece: Supporting the painting's study and conservation treatment 2023Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review 2019Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the Ghent Altarpiece 2022Neural network-based classification of X-ray fluorescence spectra of artists’ pigments: an approach leveraging a synthetic dataset created using the fundamental parameters method 2021Artificial Intelligence for Pigment Classification Task in the Short-Wave Infrared Range 2024A Review on Traditional and Artificial Intelligence-Based Preservation Techniques for Oil Painting Artworks

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