162969 20251017144653.0 doi 10.3390/math13142304 sideral 145420 ART-2025-145420 eng Salmeron, Jose L. Physically Informed Synthetic Data Generation and U-Net Generative Adversarial Network for Palimpsest Reconstruction 2025 Access copy available to the general public Unrestricted 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. info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Fernandez-Palop, Eva Universidad de Zaragoza 5014 690 Universidad de Zaragoza Unidad Predepartam. Bellas Ar. Área Pintura 13, 14 (2025), 2304 [21 pp.] Mathematics (Basel) Mathematics 2227-7390 2194041 http://zaguan.unizar.es/record/162969/files/texto_completo.pdf Versión publicada 2420094 http://zaguan.unizar.es/record/162969/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:162969 articulos driver 2025-10-17-14:37:12 ARTICLE