High-Fidelity Mural Restoration via a Unified Hybrid Mask-Aware Transformer
Ancient murals are valuable cultural artifacts, but many have suffered severe degradation due to environmental exposure, material aging, and human activity. Restoring these artworks is challenging because it requires both reconstructing large missing structures and preserving authentic, undamaged regions. We present the Hybrid Mask-Aware Transformer (HMAT), a unified framework for high-fidelity mural restoration that addresses both structural completion and authentic-region preservation. HMAT integrates Mask-Aware Dynamic Filtering for robust local texture modeling with a Transformer bottleneck for long-range structural inference, enabling recovery of continuous line patterns and coherent mural structures under irregular damage. To handle diverse degradation morphologies, we introduce a mask-conditional style fusion module that adapts the generative process according to the shape and extent of missing regions. We also propose a fidelity-oriented training objective that combines hole-normalized reconstruction, discriminator feature matching, and high-receptive-field perceptual supervision to improve damaged-region fidelity, texture consistency, and boundary quality. In addition, we analyze a Teacher-Forcing Decoder with hard-gated skip connections as a feature-space boundary-conditioning strategy. Experiments show that HMAT matches or outperforms representative convolutional, transformer-based, and edge-guided inpainting baselines, with especially strong gains in perceptual realism and severe-mask settings. Ablation studies further identify the proposed objective, MADF-based mask-aware encoding, and mask-conditioned synthesis as the main contributors to restoration quality. These results demonstrate that HMAT provides an effective and competitive solution for cultural heritage mural restoration.