{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T10:14:20Z","timestamp":1781777660239,"version":"3.54.5"},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"name":"National Key Research and Development Program of China","award":["2022YFF0901902"],"award-info":[{"award-number":["2022YFF0901902"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62171324"],"award-info":[{"award-number":["62171324"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Comput. Cult. Herit."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            Mural image inpainting is far less explored compared to its natural image counterpart and remains largely unsolved. Most existing image-inpainting methods tend to take the target image as the only input and directly repair the damage to generate a visually plausible result. These methods obtain high performance in restoration or completion of some pre-defined objects, e.g., human face, fabric texture, and printed texts, etc., however, are not suitable for repairing murals with varying subjects and large damaged areas. Moreover, due to discrete colors in paints, mural inpainting may suffer from apparent color bias. To this end, in this article, we propose a line drawing guided progressive mural inpainting method. It divides the inpainting process into two steps: structure reconstruction and color correction, implemented by a structure reconstruction network (SRN) and a color correction network (CCN), respectively. In structure reconstruction, SRN utilizes the line drawing as an assistant to achieve large-scale content authenticity and structural stability. In color correction, CCN operates a local color adjustment for missing pixels which reduces the negative effects of color bias and edge jumping. The proposed approach is evaluated against the current state-of-the-art image inpainting methods. Qualitative and quantitative results demonstrate the superiority of the proposed method in mural image inpainting. The codes and data are available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/qinnzou\/mural-image-inpainting\">https:\/\/github.com\/qinnzou\/mural-image-inpainting<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3737703","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T12:13:57Z","timestamp":1748520837000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Line Drawing-Guided Progressive Inpainting for Mural Damage"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4049-1337","authenticated-orcid":false,"given":"Luxi","family":"Li","sequence":"first","affiliation":[{"name":"Intelligent Computing Laboratory for Cultural Heritage, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7955-0782","authenticated-orcid":false,"given":"Qin","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5530-6945","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of LIESMARS, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5383-8913","authenticated-orcid":false,"given":"Hongkai","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, Ohio, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4925-0572","authenticated-orcid":false,"given":"Long","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2002-6115","authenticated-orcid":false,"given":"Chengfang","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan,\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9186-8311","authenticated-orcid":false,"given":"Xianfeng","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of LIESMARS, Wuhan University, Wuhan,\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1100-7294","authenticated-orcid":false,"given":"Xiaoguang","family":"Wang","sequence":"additional","affiliation":[{"name":"Intelligent Computing Laboratory for Cultural Heritage, Wuhan University, Wuhan,\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1551-3610","authenticated-orcid":false,"given":"Qingquan","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"978","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Cai Jiayin","year":"2022","unstructured":"Jiayin Cai, Changlin Li, Xin Tao, and Yu-Wing Tai. 2022. 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