A coarse-to-fine restoration framework integrating deep semantic inpainting and texture-aware refinement for Jinling school landscapes

Traditional Chinese ink paintings on paper or silk are highly susceptible to degradation. Over time, physical decay such as creases not only damages the surface but also obscures the original brushwork. Virtual restoration, as a non-contact digital intervention, has emerged as a vital tool for heritage preservation. Yet, generic generative models-most notably GANs and Diffusion-often struggle with the dense, layered textures of the Jinling School(), particularly the Jimofa () technique. GANs tend to "hallucinate" details that clash with traditional brushwork logic, while Latent-based models can drift toward a modern aesthetic that feels disconnected from the original archaic spirit. To address these discrepancies, we propose a coarse-to-fine framework specifically calibrated for Jinling School landscapes. This coarse-to-fine architecture mirrors the traditional 'Bone-first, Ink-second' painting methodology of the Jinling School. By decoupling structural recovery (Skeleton) from texture deposition (Flesh), our computational process aligns physically with the artifact's original creation logic. Initially, a deep convolutional network restores macroscopic structural continuity, effectively smoothing creases and reclaiming the mountain's geometric silhouette. This is followed by a texture-aware refinement module that uses manifold texture grafting to inject high-frequency details into otherwise blurred regions. Experimental results indicate that, beyond restoring overall continuity, the framework appears able to recover aspects of the high-frequency "ink noise" and deep tonal peaks traditionally associated with the Jimofa technique. Crucially, comparative analysis confirms that the framework significantly reduces the risk of 'semantic hallucination' (e.g., the fabrication of non-existent objects) prevalent in large-scale generative models, ensuring distinct historical fidelity. Quantitative assessments-specifically average gradient and edge density-show a measurable improvement over baseline models, all while the system maintains a strict adherence to the principle of "minimal intervention" in undamaged areas. By mitigating the over-smoothing typical of conventional deep learning, this work suggests a path for the digital restoration of rare, small-sample artworks that seeks to balance visual plausibility with historical rigor.