From Traditional Craft to Digital Restoration: An Intelligent Rebirth of Ancient Chinese Painting Restoration Technique

From Traditional Craft to Digital Restoration: An Intelligent Rebirth of Ancient Chinese Painting Restoration Technique | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article From Traditional Craft to Digital Restoration: An Intelligent Rebirth of Ancient Chinese Painting Restoration Technique Ying Zhang, Kewen Zhu, Zejian Li, Zhongni Liu, Kaixin Jia, Jiesi Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6582659/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Apr, 2026 Read the published version in Humanities and Social Sciences Communications → Version 1 posted 10 You are reading this latest preprint version Abstract Chinese painting restoration blends artistry and heritage preservation. While AI offers new solutions, current methods struggle to replicate its unique style and often overlook restorers' practical needs. This gap hinders the effective preservation of its historical and cultural value. To address this, our study explores AI’s potential in this field, developing InkRenew, an AI-assisted restoration system. Leveraging deep learning, InkRenew integrates dynastic and stylistic knowledge to provide real-time restoration guidance. A controlled experiment with 34 novice restorers compared AI-assisted and traditional methods, evaluating restoration quality, accuracy, and user experience. The results showed that InkRenew significantly enhanced efficiency and precision while reducing operational burden, demonstrating high usability and acceptance. Theoretically, this work bridges AI and traditional restoration, proposing a digital preservation framework. Practically, it offers restorers an efficient tool, and culturally, it advances intelligent heritage conservation. This research provides an innovative approach to safeguarding and disseminating cultural heritage. Business and commerce/Information systems and information technology Humanities/Cultural and media studies Ancient Painting Restoration Cultural Heritage Generative AI Digitization Human-AI interaction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Apr, 2026 Read the published version in Humanities and Social Sciences Communications → Version 1 posted Editorial decision: Revision requested 11 Sep, 2025 Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviews received at journal 20 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviewers invited by journal 30 May, 2025 Editor invited by journal 30 May, 2025 Editor assigned by journal 28 May, 2025 Submission checks completed at journal 28 May, 2025 First submitted to journal 03 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6582659","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":465095026,"identity":"a962ac01-328b-4764-b331-581baff3f2fe","order_by":0,"name":"Ying 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