Digital restoration of ancient Jiangnan murals via proxy learning and structural guidance

This article proposes a structure guided proxy restoration (SGPR) workflow to address the problem of the lack of real paired data in the digital restoration of ancient murals in Jiangnan. This method adopts a dual track strategy, training a generative model (ArtBooth) on a Chinese classical painting dataset through proxy learning to master artistic styles, and combining it with a non training selective feature extraction (SFE) algorithm to directly extract structural information from damaged murals. The two are integrated through an optimized multi-condition control network (OptCtrl) to achieve high fidelity repair. Experiments on simulated and real murals show that this method outperforms existing mainstream methods in both objective indicators and expert blind evaluations. This study provides a data-efficient new paradigm for intelligent restoration of cultural heritage, breaking through the limitations of traditional reliance on professional training data.