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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\u2014most notably GANs and Diffusion\u2014often struggle with the dense, layered textures of the Jinling School(\u91d1\u9675\u756b\u6d3e), particularly the\n                    <jats:italic>Jimofa<\/jats:italic>\n                    (\u7a4d\u58a8\u6cd5) technique. GANs tend to \u201challucinate\u201d 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 \u2018Bone-first, Ink-second\u2019 painting methodology of the Jinling School. By decoupling structural recovery (Skeleton) from texture deposition (Flesh), our computational process aligns physically with the artifact\u2019s original creation logic. Initially, a deep convolutional network restores macroscopic structural continuity, effectively smoothing creases and reclaiming the mountain\u2019s 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 \u201cink noise\u201d and deep tonal peaks traditionally associated with the\n                    <jats:italic>Jimofa<\/jats:italic>\n                    technique. Crucially, comparative analysis confirms that the framework significantly reduces the risk of \u2018semantic hallucination\u2019 (e.g., the fabrication of non-existent objects) prevalent in large-scale generative models, ensuring distinct historical fidelity. Quantitative assessments\u2014specifically average gradient and edge density\u2014show a measurable improvement over baseline models, all while the system maintains a strict adherence to the principle of \u201cminimal intervention\u201d 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.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-49966-2","type":"journal-article","created":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T09:14:09Z","timestamp":1777194849000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A coarse-to-fine restoration framework integrating deep semantic inpainting and texture-aware refinement for Jinling school landscapes"],"prefix":"10.1038","volume":"16","author":[{"given":"Chuan","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,26]]},"reference":[{"key":"49966_CR1","first-page":"92","volume":"28","author":"JY Zhou","year":"2012","unstructured":"Zhou, J. 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