000162969 001__ 162969
000162969 005__ 20251017144653.0
000162969 0247_ $$2doi$$a10.3390/math13142304
000162969 0248_ $$2sideral$$a145420
000162969 037__ $$aART-2025-145420
000162969 041__ $$aeng
000162969 100__ $$aSalmeron, Jose L.
000162969 245__ $$aPhysically Informed Synthetic Data Generation and U-Net Generative Adversarial Network for Palimpsest Reconstruction
000162969 260__ $$c2025
000162969 5060_ $$aAccess copy available to the general public$$fUnrestricted
000162969 5203_ $$aThis 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.
000162969 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000162969 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000162969 700__ $$aFernandez-Palop, Eva$$uUniversidad de Zaragoza
000162969 7102_ $$15014$$2690$$aUniversidad de Zaragoza$$bUnidad Predepartam. Bellas Ar.$$cÁrea Pintura
000162969 773__ $$g13, 14 (2025), 2304 [21 pp.]$$pMathematics (Basel)$$tMathematics$$x2227-7390
000162969 8564_ $$s2194041$$uhttps://zaguan.unizar.es/record/162969/files/texto_completo.pdf$$yVersión publicada
000162969 8564_ $$s2420094$$uhttps://zaguan.unizar.es/record/162969/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000162969 909CO $$ooai:zaguan.unizar.es:162969$$particulos$$pdriver
000162969 951__ $$a2025-10-17-14:37:12
000162969 980__ $$aARTICLE