The digital preservation of ancient books requires reliable restoration and enhancement of document images degraded by aging, environmental exposure, and physical damage. We propose an integrated generative framework that reconstructs corrupted regions and improves legibility while preserving structural and semantic fidelity. The core model, Generative Restoration and Enhancement Network (GREN), combines multimodal feature extraction with generative restoration and multiscale enhancement to capture both local details and global layout for high quality reconstruction. To handle spatially non-uniform degradations, we further introduce an Adaptive Restoration Strategy (ARS) that performs spatially aware degradation modeling, semantic guided enhancement, and iterative refinement to produce coherent text and background restoration. Experiments on diverse degradation scenarios demonstrate that the proposed method improves image quality, structural consistency, and readability compared with existing approaches, showing robust performance in restoring historical text and fine textures. The proposed framework provides a practical solution for large scale digital archiving of ancient documents.
Ke et al. (Thu,) studied this question.