{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T02:58:55Z","timestamp":1771556335770,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National K&amp;D Program of China","award":["2022YFF0904400"],"award-info":[{"award-number":["2022YFF0904400"]}]},{"name":"the National K&amp;D Program of China","award":["42171356"],"award-info":[{"award-number":["42171356"]}]},{"name":"the National K&amp;D Program of China","award":["42171444"],"award-info":[{"award-number":["42171444"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFF0904400"],"award-info":[{"award-number":["2022YFF0904400"]}]},{"name":"National Natural Science Foundation of China","award":["42171356"],"award-info":[{"award-number":["42171356"]}]},{"name":"National Natural Science Foundation of China","award":["42171444"],"award-info":[{"award-number":["42171444"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Mural paintings, as the main components of painted cultural relics, have essential research value and historical significance. Due to their age, murals are easily damaged. Obtaining intact sketches is the first step in the conservation and restoration of murals. However, sketch extraction often suffers from problems such as loss of details, too thick lines, or noise interference. To overcome these problems, a mural sketch extraction method based on image enhancement and edge detection is proposed. The experiments utilize Contrast Limited Adaptive Histogram Equalization (CLAHE) and bilateral filtering to enhance the mural images. This can enhance the edge features while suppressing the noise generated by over-enhancement. Finally, we extract the refined sketch of the mural using the Laplacian Edge with fine noise remover (FNR). The experimental results show that this method is superior to other methods in terms of visual effect and related indexes, and it can extract the complex line regions of the mural.<\/jats:p>","DOI":"10.3390\/s24072213","type":"journal-article","created":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T13:32:56Z","timestamp":1711891976000},"page":"2213","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A New Method for Extracting Refined Sketches of Ancient Murals"],"prefix":"10.3390","volume":"24","author":[{"given":"Zhiji","family":"Yu","sequence":"first","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"},{"name":"Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, Beijing 102616, China"}]},{"given":"Shuqiang","family":"Lyu","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"},{"name":"Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, Beijing 102616, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0942-629X","authenticated-orcid":false,"given":"Miaole","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"},{"name":"Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, Beijing 102616, China"}]},{"given":"Yutong","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"},{"name":"Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, Beijing 102616, China"}]},{"given":"Lihong","family":"Li","sequence":"additional","affiliation":[{"name":"Yungang Research Institute, Datong 037007, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mallick, P.K., Meher, P., Majumder, A., and Das, S.K. (2020). Electronic Systems and Intelligent Computing, Springer. Lecture Notes in Electrical, Engineering.","DOI":"10.1007\/978-981-15-7031-5"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sun, D., Zhang, J., Pan, G., and Zhan, R. (2018, January 23\u201327). Mural2Sketch: A Combined Line Drawing Generation Method for Ancient Mural Painting. Proceedings of the 2018 IEEE International Conference on Multimedia and Expo (ICME), San Diego, CA, USA.","DOI":"10.1109\/ICME.2018.8486504"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Computer Vision\u2013ECCV 2014, Springer. ECCV 2014. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-10593-2"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","article-title":"Contour Detection and Hierarchical Image Segmentation","volume":"33","author":"Maire","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","unstructured":"Shen, W., Wang, X., Wang, Y., Bai, X., and Zhang, Z. (2015, January 7\u201312). DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"He, J., Zhang, S., Yang, M., Shan, Y., and Huang, T. (2019, January 15\u201320). Bi-Directional Cascade Network for Perceptual Edge Detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00395"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s11263-017-1004-z","article-title":"Holistically-nested edge detection","volume":"125","author":"Xie","year":"2017","journal-title":"Int. J. Comput. Vis."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pan, G., Sun, D., Zhan, R., and Zhang, J. (2018, January 11\u201314). Mural sketch generation via style-aware convolutional neural network. Proceedings of the Computer Graphics International 2018, Bintan Island, Indonesia.","DOI":"10.1145\/3208159.3208160"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cheng, M.-M., Hu, X., Wang, K., and Bai, X. (2017, January 21\u201326). Richer Convolutional Features for Edge Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.622"},{"key":"ref_10","first-page":"1712258","article-title":"Distance Field-Based Convolutional Neural Network for Edge Detection","volume":"2022","author":"Hu","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_11","first-page":"11","article-title":"Deep Learning for the Extraction of Sketches from Spectral Images of Historical Paintings","volume":"Volume 11784","author":"Zhang","year":"2021","journal-title":"Proceedings of the Optics for Arts, Architecture, and Archaeology VIII"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Riba, E., Mishkin, D., Ponsa, D., Rublee, E., and Bradski, G. (2020, January 1\u20135). Kornia: An Open Source Differentiable Computer Vision Library for PyTorch. Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093363"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"68281","DOI":"10.1109\/ACCESS.2022.3186344","article-title":"LDC: Lightweight Dense CNN for Edge Detection","volume":"10","author":"Soria","year":"2022","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6493","DOI":"10.1007\/s00521-018-3475-4","article-title":"Face sketch recognition using a hybrid optimization model","volume":"31","author":"Samma","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hallman, S., and Fowlkes, C. (2015, January 7\u201312). Oriented edge forests for boundary detection. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298782"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108446","DOI":"10.1016\/j.patcog.2021.108446","article-title":"Face photo-sketch synthesis via full-scale identity supervision","volume":"124","author":"Bing","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bertasius, G., Shi, J., and Torresani, L. (2015, January 7\u201312). DeepEdge: A multi-scale bifurcated deep network for top-down contour detection. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299067"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Soria, X., Riba, E., and Sappa, A. (2020, January 1\u20135). Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection. Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093290"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pu, M., Huang, Y., Guan, Q., and Ling, H. (2021, January 10\u201317). RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00680"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Vento, M., and Percannella, G. (2019). Computer Analysis of Images and Patterns, Springer. CAIP 2019. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-29888-3"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109586","DOI":"10.1016\/j.patcog.2023.109586","article-title":"GGD-GAN: Gradient-Guided dual-Branch adversarial networks for relic sketch generation","volume":"141","author":"Wang","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108077","DOI":"10.1016\/j.patcog.2021.108077","article-title":"IsGAN: Identity-sensitive generative adversarial network for face photo-sketch synthesis","volume":"119","author":"Yan","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111057","DOI":"10.1016\/j.knosys.2023.111057","article-title":"Multi-scale pseudo labeling for unsupervised deep edge detection","volume":"280","author":"Zhou","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s11263-021-01539-8","article-title":"Semantic Edge Detection with Diverse Deep Supervision","volume":"130","author":"Liu","year":"2022","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"26680","DOI":"10.1021\/acsomega.2c02858","article-title":"Flame Edge Detection Method Based on a Convolutional Neural Network","volume":"7","author":"Sun","year":"2022","journal-title":"ACS Omega"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Su, Z., Liu, W., Yu, Z., Liao, Q., Tian, Q., Pietik\u00e4inen, M., and Liu, L. (2021, January 10\u201317). Pixel Difference Networks for Efficient Edge Detection. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00507"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2213\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:21:13Z","timestamp":1760106073000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2213"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,29]]},"references-count":26,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24072213"],"URL":"https:\/\/doi.org\/10.3390\/s24072213","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,29]]}}}