Muography provides a non-invasive method for exploring the internal structures of cultural heritage artifacts using cosmic-ray-derived muons. In this work, we present an integrated pipeline combining synthetic data generation via Monte Carlo simulations with advanced deep learning techniques, aiming to overcome traditional limitations in muographic imaging. Utilizing the Geant4 toolkit, muon interactions were simulated within materials such as concrete, limestone, and wood, including concealed metallic elements to replicate realistic structural scenarios. To enhance image quality without requiring prolonged exposure times and to reduce detector costs, our approach employs two neural networks sequentially: an event augmentation network based on a U-Net architecture enriched with residual dense blocks, and a resolution augmentation network designed to improve spatial detail.