A Dunhuang mural restoration network based on mask guidance and Transformer architecture.
In the process of Dunhuang mural restoration, models often struggle to focus on damaged areas, leading to issues such as color deviation and blurred lines. To address these problems, we propose a Dunhuang mural restoration network based on mask guidance and Transformer architecture, named MGTNet. Fi...
| Published in: | Digital Scholarship in the Humanities Vol. 40; no. 3; pp. 1051 - 1064 |
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| Main Authors: | , , , , |
| Format: | Article |
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Oxford University Press / USA
Sep2025
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=188027895&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 188027895 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Sep2025 vid: 40 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 188027895 10.1093/llc/fqaf044 ppf: 1051 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.4MB tig: atl: A Dunhuang mural restoration network based on mask guidance and Transformer architecture. aug: au: Zhang, Qiang Ren, Doudou Gao, Ying Zhang, Yixuan Chen, Tao affil: School of Liberal Arts, Huaiyin Normal University, Huaian 223300, China School of Humanities and Social Development, Nanjing Agricultural University, Nanjing 210095, China School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China School of Cultural Heritage and Information Management, Shanghai University, Shanghai 200444, China School of Information Management, Sun Yat-sen University, Guangzhou 510006, China su: Art conservation & restoration Transformer models Artificial intelligence Digital humanities Image reconstruction Cultural property Image enhancement (Imaging systems) Dunhuang (China) sug: subj: Dunhuang (China) Art conservation & restoration Transformer models Artificial intelligence Digital humanities Image reconstruction Cultural property Image enhancement (Imaging systems) keyword: adaptive attention deep learning digital humanities Dunhuang mural restoration mask guidance Transformer ab: In the process of Dunhuang mural restoration, models often struggle to focus on damaged areas, leading to issues such as color deviation and blurred lines. To address these problems, we propose a Dunhuang mural restoration network based on mask guidance and Transformer architecture, named MGTNet. First, we design a mural focus attention module to dynamically optimize both channel and spatial information in an adaptive manner. Next, we introduce a mural enhancement module that leverages the long-range dependency capturing capability of Transformers to improve restoration quality. Finally, a mask-guided downsampling module is proposed, which fuses the mask image as prior knowledge with downsampled features, enhancing the model's ability to perceive damaged areas. Experiments on a publicly available Dunhuang mural dataset demonstrate that the proposed method outperforms comparison algorithms in terms of objective evaluation metrics such as PSNR and SSIM, validating the effectiveness of the algorithm. This study demonstrates the practical application potential of the algorithm in Dunhuang mural restoration and contributes to the significant advancements of artificial intelligence in the field of digital humanities. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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