Physically Informed Synthetic Data Generation and U-Net Generative Adversarial Network for Palimpsest Reconstruction

Jose L. Salmeron () and Eva Fernandez-Palop
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Jose L. Salmeron: School of Engineering, CUNEF University, 28040 Madrid, Spain
Eva Fernandez-Palop: Department of Arts, Universidad de Zaragoza, 44003 Teruel, Spain

Mathematics, 2025, vol. 13, issue 14, 1-21

Abstract: This paper introduces a novel adversarial learning framework for reconstructing hidden layers in historical palimpsests. Recovering text hidden in historical palimpsests is complicated by various artifacts, such as ink diffusion, degradation of the writing substrate, and interference between overlapping layers. To address these challenges, the authors of this paper combine a synthetic data generator grounded in physical modeling with three generative architectures: a baseline VAE, an improved variant with stronger regularization, and a U-Net-based GAN that incorporates residual pathways and a mixed loss strategy. The synthetic data engine aims to emulate key degradation effects—such as ink bleeding, the irregularity of parchment fibers, and multispectral layer interactions—using stochastic approximations of underlying physical processes. The quantitative results suggest that the U-Net-based GAN architecture outperforms the VAE-based models by a notable margin, particularly in scenarios with heavy degradation or overlapping ink layers. By relying on synthetic training data, the proposed method facilitates the non-invasive recovery of lost text in culturally important documents, and does so without requiring costly or specialized imaging setups.

Keywords: palimpsest reconstruction; generative adversarial networks; deep learning; synthetic data generation; cultural heritage; multispectral imaging (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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