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A Review About Virtual Restoration of Ancient Murals

Yıl 2025, Cilt: 18 Sayı: 1, 31 - 43, 26.06.2025
https://doi.org/10.54525/bbmd.1558102

Öz

Ancient murals provide us invaluable information about the history, culture and art of the people who painted them in the past. But damaged murals limit our knowledge so if it is possible restoration of the ancient mural is mandatory. Since these murals are immovable cultural heritages, they need to be restored in situ. Physical restoration is a high-risk process, and a restoration mistake can irreversibly damage a thousand-year-old mural. Nowadays, thanks to the increasing computational capabilities, significant advancements have been achieved in image processing techniques. Today, virtual restoration of ancient murals could be done with the support of digital image processing techniques. Thanks to virtual restoration, restoration preview and guidance for the restoration artists can be performed without any intervention to the original mural. In this way, the physical restoration process can be carried out more accurately. Image processing techniques are used in digital archiving of ancient murals, enhancement of details and color in faded murals, inpainting missing parts of faint murals, analyzing superimposed motifs, removing cracks, and identifying the materials used for painting. In this review, digital image processing techniques used in the virtual restoration of ancient murals are discussed.

Kaynakça

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Antik Duvar Resimlerinin Sanal Restorasyonu Hakkında Bir İnceleme

Yıl 2025, Cilt: 18 Sayı: 1, 31 - 43, 26.06.2025
https://doi.org/10.54525/bbmd.1558102

Öz

Antik duvar resimleri bize geçmişte onları yaratan insanların tarihi, kültürü ve sanatı hakkında paha biçilmez bilgiler sağlar. Ancak hasarlı duvar resimleri onlardan elde edebileceğimiz bilgiyi sınırlar, bu nedenle mümkünse antik duvar resimlerinin restorasyonu gereklidir. Duvar resimleri taşınmaz kültürel varlıkları olduğundan, yerinde restore edilmeleri gerekir. Fiziksel restorasyon riskli bir işlemdir ve restorasyonda yapılacak bir hata bin yıllık bir duvar resmine geri dönülemez zararlar verebilir. Günümüzde bilgisayar tabanlı hesaplama yeteneklerinin artmasıyla görüntü işleme tekniklerinde önemli ilerlemeler kaydedilmiştir. Bugün dijital görüntü işleme tekniklerinin desteğiyle antik duvar resimlerinin sanal restorasyonu yapılabilmektedir. Sanal restorasyon sayesinde, orijinal duvar resmine fiziki hiçbir müdahalede bulunulmadan restorasyon ön izlemesi ve restorasyon sanatçılarına rehberlik yapılabilmektedir. Bu sayede fiziksel restorasyon süreci daha sağlıklı bir şekilde gerçekleştirilebilmektedir. Görüntü işleme teknikleri, antik duvar resimlerinin dijital arşivlenmesinde, soluk duvar resimlerindeki detayların ve renklerin belirginleştirilmesinde, yıpranmış duvar resimlerinde eksik kısımların tamamlanmasında, üst üste binen motiflerin analizinde, resim yüzeyindeki çatlakların giderilmesinde ve boyamada kullanılan malzemelerin belirlenmesinde kullanılmaktadır. Bu derlemede antik duvar resimlerinin sanal restorasyonunda kullanılan dijital görüntü işleme teknikleri ele alınmaktadır.

Kaynakça

  • Fredlund, G. ve Sundstrom, L. “Digital infra-red photography for recording painted rock art”, Antiquity, c. 81, sy 313, ss.733-742, 2007, doi: 10.1017/S0003598X00095697.
  • Zeng Y. ve YGong, Y. “Nearest Neighbor based DigitalRestoration of Damaged Ancient Chinese Paintings”, içinde2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Kas. 2018, ss. 1-5. doi:10.1109/ICDSP.2018.8631553.
  • Jiang, D., Li, P, ve Xie, H. “Research into Digital Oil PaintingRestoration Algorithm Based on Image AcquisitionTechnology”, içinde 2022 International Conference on 3DImmersion, Interaction and Multi-sensory Experiences(ICDIIME), Madrid, Spain: IEEE, Haz. 2022, ss. 65-68. doi:10.1109/ICDIIME56946.2022.00022.
  • Nikolaidis N. ve Pitas, I. “Digital image processing in painting restoration and archiving”, içinde Proceedings 2001International Conference on Image Processing (Cat.No.01CH37205), Eki. 2001, ss. 586-589 c.1. doi: 10.1109/ICIP.2001.959084.
  • Pappas M. ve Pitas, I. “Digital color restoration of oldpaintings”, IEEE transactions on image processing : apublication of the IEEE Signal Processing Society, c. 9, ss. 291-4, Şub. 2000, doi: 10.1109/83.821745.
  • David, B. Brayer, J., Mcniven, I. ve Watchman, A. L. , “Why digital enhancement of rock paintings works: Rescaling andsaturating colours”, Antiquity, c. 75, ss. 781-792, Ara. 2015,doi: 10.1017/S0003598X00089286.
  • Barni, M., Pelagotti, A. ve Piva, A., “Image processing for theanalysis and conservation of paintings: opportunities andchallenges”, IEEE Signal Processing Magazine, c. 22, sy 5, ss.141-144, Eyl. 2005, doi: 10.1109/MSP.2005.1511835.
  • Xu W. ve Fu, Y., “Deep learning algorithm in ancient relicsimage colour restoration technology”, Multimed Tools Appl,c. 82, sy 15, ss. 23119-23150, Haz. 2023, doi: 10.1007/s11042-022-14108-z.
  • Ciortan, I. M. George, S. ve Hardeberg, J. Y. , “Colour-Balanced Edge-Guided , Digital Inpainting: Applications onArtworks”, Sensors, c. 21, sy 6, Art. sy 6, Oca. 2021, doi:10.3390/s21062091.
  • Zeng, Y., Gong, Y. ve Zeng, X. “Controllable digital restorationof ancient paintings using convolutional neural network andnearest neighbor”, Pattern Recognition Letters, c. 133, ss.158-164, May. 2020, doi: 10.1016/j.patrec.2020.02.033.
  • Xiao, Q. Li, G. ve Chen, Q. “Image inpainting network forfilling large missing regions using residual gather”, Expert Systems with Applications, c. 183, s. 115381, Kas. 2021, doi:10.1016/j.eswa.2021.115381.
  • Qin, Z., Zeng, Q. Zong, Y. ve Xu, F. “Image inpainting based on deep learning: A review”, Displays, c. 69, s. 102028, Eyl.2021, doi: 10.1016/j.displa.2021.102028.
  • [13]Chinese paintings using color contrast enhancement and lacuna texture synthesis”, IEEE Transactions on Image Processing, c. 13, sy 3, ss. 416-429, Mar. 2004, doi:10.1109/TIP.2003.821347.
  • Ge, H. , Yu, Y. ve Zhang, L. “A virtual restoration network ofancient murals via global–local feature extraction andstructural information guidance”, Herit Sci, c. 11, sy 1, s. 264,Ara. 2023, doi: 10.1186/s40494-023-01109-w.
  • Tint W. ve Tin, M. M. “Digital Restoration of Ancient Murals:Assessing the Efficacy of Coherent Transport Inpainting withDamage Ratio Analysis”, içinde 2024 IEEE Conference onComputer Applications (ICCA), Mar. 2024, ss. 1-8. doi:10.1109/ICCA62361.2024.10532855.
  • Singh, U., Maiti, S., Saini, A. ve Dhiraj, “Ancient Indian Murals Digital Restoration through Image InPainting”, içinde 202310th International Conference on Signal Processing andIntegrated Networks (SPIN), Mar. 2023, ss. 635-640. doi: 10.1109/SPIN57001.2023.10116111.
  • Yan, Y. , Zhang, R., He, H. Lei, T., Zhang, X. ve Jiang, C. “Image Restoration Technology of Tang Dynasty Tomb Murals UsingAdversarial Edge Learning”, J. Comput. Cult. Herit., c. 17, sy 3, s.52:1-52:11, Eyl. 2024, doi: 10.1145/3674984.
  • Kumar P. ve Gupta, V. “Restoration of damaged artworksbased on a generative adversarial network”, Multimed Tools Appl, c. 82, sy 26, ss. 40967-40985, Kas. 2023, doi:10.1007/s11042-023-15222-2.
  • [19]Challenge”, International Journal of Computer Vision, c. 115, sy 3, ss. 211-252, Ara. 2015, doi:10.1007/s11263-015-0816-y.
  • Zhou, B., Lapedriza, A., Khosla, A., Oliva, A. ve Torralba, A.“Places: A 10 Million Image Database for Scene Recognition”,IEEE Transactions on Pattern Analysis and MachineIntelligence, c. 40, sy 6, ss. 1452-1464, Haz. 2018, doi:10.1109/TPAMI.2017.2723009.
  • Liu, Z., Luo, P., Wang, X. ve Tang, X. “Deep Learning FaceAttributes in the Wild”, içinde 2015 IEEE International Conference on Computer Vision (ICCV), Ara. 2015, ss. 3730-3738. doi: 10.1109/ICCV.2015.425.
  • Doersch, C., Singh, S., Gupta, A., Sivic, J. ve Efros, A. A. “Whatmakes Paris look like Paris?”, ACM Trans. Graph., c. 31, sy 4,Tem. 2012, doi: 10.1145/2185520.2185597.
  • Tylecek R. ve Sára, R. “patt Pattern Templates for Recognitionof Objects with Regular Structure”, içinde PatternRecognition: 35th German Conference, GCPR 2013, Saarbrücken, Germany, September 3-6, 2013. Proceedings,Springer, 2013, ss. 364-374. doi: 10.1007/978-3-642-40602-7_39.
  • Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S. ve Vedaldi, A.“Describing Textures in the Wild”, içinde 2014 IEEEConference on Computer Vision and Pattern Recognition, Haz. 2014, ss. 3606-3613. doi: 10.1109/CVPR.2014.461.
  • Harman, J. “Using Decorrelation Stretch to Enhance Rock ArtImages”, program adı: American Rock Art ResearchAssociation Annual Meeting, May. 2005. Erişim: 21 Şubat 2023. [Çevrimiçi]. Erişim adresi: https://www.dstretch.com/AlgorithmDescription.html
  • Le Quellec, J.-L., Duquesnoy, F. ve Defrasne, C. “Digital imageenhancement with DStretch®: Is complexity always necessary for efficiency?”, Digital Applications in Archaeology andCultural Heritage, c. 2, sy 2, ss. 55-67, Oca. 2015, doi:10.1016/j.daach.2015.01.003.
  • Mark R. ve Billo, E. “Application of Digital Image Enhancement in Rock Art Recording”, American Indian Rock Art, c. 28, 2002.
  • Defrasne, C. “Digital image enhancement for recordingrupestrian engravings: applications to an alpine rockshelter”,Journal of Archaeological Science, c. 50, ss. 31-38, Eki. 2014,doi: 10.1016/j.jas.2014.06.010.
  • Cerrillo Cuenca E. ve Sepúlveda, M. “An assessment of methods for the digital enhancement of rock paintings: therock art from the precordillera of Arica (Chile) as a case study”, Journal of Archaeological Science, c. 55, ss. 197-208, Mar. 2015, doi: 10.1016/j.jas.2015.01.006.
  • Sun, P., Hou, M., Lyu, S., Wang, W., Shaker, A. ve Li, S.“Virtual cleaning of sooty murals in ancient temples usingtwice colour attenuation prior”, Computers & Graphics, c.120, s. 103924, May. 2024, doi: 10.1016/j.cag.2024.103924.
  • Gao, Z., Du, M., Cao, N., Hou, M., Wang, W. ve Lyu, S.“Application of hyperspectral imaging technology to digitallyprotect murals in the Qutan temple”, Heritage Science, c. 11, sy 1, s. 8, Oca. 2023, doi: 10.1186/s40494-022-00847-7.
  • Xu Z. ve Geng, C. “Color restoration of mural images basedon a reversible neural network: leveraging reversible residual networks for structure and texture preservation”, Herit Sci, c. 12, sy 1, s. 351, Eki. 2024, doi: 10.1186/s40494-024-01471-3.
  • Giakoumis I. ve Pitas, I. “Digital Restoration of Painting Cracks”, program adı: IEEE International Symposium onCircuits and Systems, May. 1998, ss. 269-272 c.4. doi:10.1109/ISCAS.1998.698812.
  • Wang, W. “An edge based segmentation algorithm for rockfracture tracing”, içinde International Conference on Computer Graphics, Imaging and Visualization (CGIV’05),Tem. 2005, ss. 43-48. doi: 10.1109/CGIV.2005.16.
  • Wang, W., Liao, H. ve Huang, Y. “Rock fracture tracing based on image processing and SVM”, Third International Conference on Natural Computation (ICNC 2007), Ağu. 2007, ss. 632-635. doi: 10.1109/ICNC.2007.643.
  • Qiao, K., Hou, M., Lyu, S. ve Li, L. “Extraction and restorationof scratched murals based on hyperspectral imaging—a casestudy of murals in the East Wall of the sixth grotto of YungangGrottoes, Datong, China”, Herit Sci, c. 12, sy 1, s. 123, Nis. 2024, doi: 10.1186/s40494-024-01215-3.
  • Tuncay, E. “Rock rupture phenomenon and pillar failure intuffs in the Cappadocia region (Turkey)”, InternationalJournal of Rock Mechanics and Mining Sciences, c. 46, sy 8, ss. 1253-1266, Ara. 2009, doi: 10.1016/j.ijrmms.2009.01.011.
  • Cornelis B. ve ark, “Crack detection and inpainting for virtual restoration of paintings: The case of the Ghent Altarpiece”,Signal Processing, c. 93, sy 3, ss. 605-619, Mar. 2013, doi:10.1016/j.sigpro.2012.07.022.
  • Sizyakin R. ark. “Crack Detection in Paintings Using Convolutional Neural Networks”, IEEE Access, c. 8, ss. 74535-74552, 2020, doi: 10.1109/ACCESS.2020.2988856.
  • Gunn, R., Ogleby, C., Lee, D. ve Whear, R. L. “A method to visually rationalise superimposed pigment motifs”, Rock Art Research, c. 27, ss. 131-136, Kas. 2010.
  • Robert, E. Petrognani, S. ve Lesvignes, E. “Applications ofdigital photography in the study of Paleolithic cave art”,Journal of Archaeological Science: Reports, c. 10, ss. 847-858, Ara. 2016, doi: 10.1016/j.jasrep.2016.07.026.
  • Pedro Javier Sosa, A. “Image analysis and treatment for thedetection of petroglyphs and their superimpositions: Rediscovering rock art in the Balos Ravine, Gran CanariaIsland”, Rock Art Research: The Journal of the Australian Rock Art Research Association (AURA), c. 40, sy 2, ss. 121-130, Kas. 2023, doi: 10.3316/informit.308401586511910.
  • Clogg, P., Dı́az-Andreu, M. ve Larkman, B. “Digital ImageProcessing and the Recording of Rock Art”, Journal ofArchaeological Science, c. 27, sy 9, ss. 837-843, Eyl. 2000, doi: 10.1006/jasc.1999.0522.
  • Casanova Municchia, A., Bartoli, F., Taniguchi, Y., Giordani,P.ve Caneva, G. “Evaluation of the biodeterioration activityof lichens in the Cave Church of Üzümlü (Cappadocia,Turkey)”, International Biodeterioration & Biodegradation, c.127, ss. 160-169, Şub. 2018, doi:10.1016/j.ibiod.2017.11.023.
  • Rogerio Candelera, M. A., Jurado, V., Laiz, L. ve Saiz Jimenez,C.“Laboratory and in situ assays of digital image analysisbased protocols for biodeteriorated rock and mural paintingsrecording”, Journal of Archaeological Science, c. 38, sy 10, ss.2571-2578, Eki. 2011, doi: 10.1016/j.jas.2011.04.020.
  • Menu M. ve Walter, P. “Prehistoric cave painting PIXE analysis for the identification of paint ‘pots’”, NuclearInstruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, c. 64, sy 1, ss. 547- Bilgisayar Bilimleri ve Mühendisliği Dergisi (2025 Cilt: 18 - Sayı: 1) - 42 552, Şub. 1992, doi: 10.1016/0168-583X(92)95531-U.
  • Crkvenjakov, D. K. “Image Processing and the Analysis ofPaintings—The Case of Serbian Baroque Icons”, IEEE BITS theInformation Theory Magazine, c. 2, sy 1, ss. 122-123, Eki.2022, doi: 10.1109/MBITS.2022.3187947.
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  • Baldia C. M. ve Jakes, K. A. “Photographic methods to detect colourants in archaeological textiles”, Journal ofArchaeological Science, c. 34, sy 4, ss. 519-525, Nis. 2007, doi:10.1016/j.jas.2006.06.010.
  • Shi, N., Yang, L., He, P., Li, Y., Wang, M. ve Zhao, H.“Research and realization of mural painting diseaserecognition methods”, içinde 2024 4th InternationalSymposium on Computer Technology and Information Science(ISCTIS), Tem. 2024, ss. 623-630. doi:10.1109/ISCTIS63324.2024.10698926.
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  • Lesvignes E. ve ark., “Using Digital Techniques to Document Prehistoric Rock Art: First Approaches on the Engraved Panelsof the Paris Basin Shelters”, Digital Applications inArchaeology and Cultural Heritage, c. 15, s. e00122, Eyl. 2019, doi: 10.1016/j.daach.2019.e00122.
  • Robinson E. ve Ware, G. “Multi-spectral Imaging of La Casade las Golondrinas Rock Paintings”, 2000. [Çevrimiçi]. Erişimadresi:http://www.famsi.org/reports/99052/99052Robinson01.pdf
  • Brady, L. M. “Documenting and Analyzing Rock, Paintingsfrom Torres Strait, NE Australia, with Digital Photography andComputer Image Enhancement”, Journal of FieldArchaeology, c. 31, sy 4, ss. 363-379, Oca. 2006, doi:10.1179/009346906791071837.
  • Brady L. M. ve Gunn, R. G. “Digital Enhancement of Deteriorated and Superimposed Pigment Art: Methods and Case Studies”, içinde A Companion to Rock Art, John Wiley &Sons, Ltd, 2012, ss. 625-643. doi: 10.1002/9781118253892.ch35.
  • Martinez, K., Cupitt, J., Saunders, D. ve Pillay, R. “Ten yearsof art imaging research”, Proceedings of the IEEE, c. 90, sy 1,ss. 28-41, Oca. 2002, doi: 10.1109/5.982403.
  • Papaodysseus, C., Exarhos, M., Panagopoulos, M.,Rousopoulos, P., Triantafillou, C. ve Panagopoulos, T. “Imageand Pattern Analysis of 1650 B.C. Wall Paintings andReconstruction”, IEEE Transactions on Systems, Man, andCybernetics - Part A: Systems and Humans, c. 38, sy 4, ss. 958-965, Tem. 2008, doi: 10.1109/TSMCA.2008.923078.
  • Xu, H., Zhang, Y. ve Zhang, J. “Frescoes restoration via virtual-real fusion: Method and practice”, Journal of CulturalHeritage, c. 66, ss. 68-75, Mar. 2024, doi:10.1016/j.culher.2023.11.001.
Toplam 58 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgi Sistemleri (Diğer)
Bölüm İnceleme
Yazarlar

Bilgin Yazlık 0000-0002-8172-3192

Erken Görünüm Tarihi 11 Haziran 2025
Yayımlanma Tarihi 26 Haziran 2025
Gönderilme Tarihi 29 Eylül 2024
Kabul Tarihi 2 Kasım 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 18 Sayı: 1

Kaynak Göster

IEEE B. Yazlık, “Antik Duvar Resimlerinin Sanal Restorasyonu Hakkında Bir İnceleme”, bbmd, c. 18, sy. 1, ss. 31–43, 2025, doi: 10.54525/bbmd.1558102.