HistoRestorer: Arquitectura Transformer para Restauracion de Murales Historicos con Metricas de Autenticidad Cultural

Digital preservation of cultural heritage requires specialized methodologies that balance technical restoration quality with historical authenticity. This work presents HistoRestorer, a Vision Transformer architecture specifically adapted for Chinese historic mural restoration. We introduce three pioneering cultural metrics: Historical Authenticity Index (HAI), Cultural Pattern Similarity (CPS), and Chromatic Fidelity Score (CFS), representing the first computational formalization for historical authenticity evaluation. Our approach employs a lightweight transformer architecture (867K parameters) achieving 181 img/s throughput on the DhMural1714 dataset (1,714 authentic images with 8,000 augmented samples). Comprehensive evaluation using 15+ validation techniques demonstrates stable training convergence with peak HAI of 0.584 and final HAI of 0.572, though a train-validation gap (0.316±0.084) indicates areas for improvement. Ablation study confirms the critical importance of the transformer component (-0.329 HAI without it). This work establishes the first computational framework for cultural authenticity metrics and provides a rigorous evaluation protocol for heritage computing applications.