Physical restoration of a painting with a digitally constructed mask

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References

  1. Stoner, J. H. & Rushfield, R. (eds.) Conservation of Easel Paintings (Routledge, 2020).

  2. Idelson, A. I. & Severini, L. in The Encyclopedia of Archaeological Sciences (ed. López Varela, S. L.) (Wiley, 2018).

  3. Corona, L. Stored collections of museums: an overview of how visible storage makes them accessible. Collect. Curation 44, 1–8 (2025).

    Article  Google Scholar 

  4. Stone, A. Treasures in the Basement? An Analysis of Collection Utilization in Art Museums. RAND dissertation series, RAND School of Public Policy (2002).

  5. Zeng, Y., Gong, Y. & Zeng, X. Controllable digital restoration of ancient paintings using convolutional neural network and nearest neighbor. Pattern Recognit. Lett. 133, 158–164 (2020).

    Article  ADS  Google Scholar 

  6. O’Brien, C., Hutson, J., Olsen, T. & Ratican, J. Limitations and possibilities of digital restoration techniques using generative AI tools: reconstituting Antoine François Callet’s Achilles Dragging Hector’s Body Past the Walls of Troy. Arts Commun. 1, 1793 (2023).

    Article  Google Scholar 

  7. Liu, X., Wan, J. & Wang, N. Ancient painting inpainting with regional attention-style transfer and global context perception. Appl. Sci. 14, 8777 (2024).

    Article  CAS  Google Scholar 

  8. Xu, Z. et al. A comprehensive dataset for digital restoration of Dunhuang murals. Sci. Data 11, 955 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Stubbs-Lee, D. A. A conservator’s investigation of museums, visible storage, and the interpretation of conservation. Collections 5, 265–323 (2009).

    Article  Google Scholar 

  10. Vecco, M. & Piazzai, M. Deaccessioning of museum collections: what do we know and where do we stand in Europe? J. Cult. Heritage 16, 221–227 (2015).

    Article  Google Scholar 

  11. Keene, S., Stevenson, A. & Monti, F. Collections for People: Museums’ Stored Collections as a Public Resource (UCL Institute of Archaeology, 2008).

  12. Jessell, B. Helmut Ruhemann’s inpainting techniques. J. Am. Inst. Conserv. 17, 1–8 (1977).

    Article  Google Scholar 

  13. Tate-Harte, A. & Thickett, D. Calculating the carbon footprint of interventive and preventive conservation at English Heritage, UK. Stud. Conserv. 69, 323–332 (2024).

    Article  CAS  Google Scholar 

  14. Johansson, E. A Detailed Conservation Report of a Heavily Retouched Painting from the Otto Valstad Collection. Master's thesis, Univ. of Oslo (2014).

  15. Scott, D. A. Art restoration and its contextualization. J. Aesthetic Educ. 51, 82–104 (2017).

    Article  Google Scholar 

  16. Amura, A. et al. Image analysis applied to the planning of a canvas painting restoration intervention. Ge-conservacion 18, 339–346 (2020).

    Article  Google Scholar 

  17. Kumar, P. & Gupta, V. Preserving artistic heritage: a comprehensive review of virtual restoration methods for damaged artworks. Arch. Comput. Methods Eng. 32, 1199–1227 (2025).

    Article  Google Scholar 

  18. Rojas, D. J. B., Fernandes, B. J. T. & Fernandes, S. M. M. A review on image inpainting techniques and datasets. In 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) 240–247 (IEEE, 2020).

  19. Yang, J. & Ruhaiyem, N. I. R. Review of deep learning-based image inpainting techniques. IEEE Access 12, 138441–138482 (2024).

    Article  Google Scholar 

  20. Barcelos, I. M., Rabelo, T. B., Bernardini, F., Monteiro, R. S. & Fernandes, L. A. F. From past to present: a tertiary investigation of twenty-four years of image inpainting. Comput. Graphics 123, 104010 (2024).

    Article  Google Scholar 

  21. Elharrouss, O., Damseh, R., Belkacem, A. N., Badidi, E. & Lakas, A. Transformer-based image and video inpainting: current challenges and future directions. Artif. Intell. Rev. 58, 124 (2025).

    Article  Google Scholar 

  22. Li, H., Hu, L., Liu, J., Zhang, J. & Ma, T. A review of advances in image inpainting research. Imaging Sci. J. 72, 669–691 (2024).

    Article  Google Scholar 

  23. Bugeau, A., Bertalmío, M., Caselles, V. & Sapiro, G. A comprehensive framework for image inpainting. IEEE Trans. Image Process. 19, 2634–2645 (2010).

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  24. Khalid, S. et al. A review on traditional and artificial intelligence-based preservation techniques for oil painting artworks. Gels 10, 517 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sizyakin, R. et al. Crack detection in paintings using convolutional neural networks. IEEE Access 8, 74535–74552 (2020).

    Article  Google Scholar 

  26. Maali Amiri, M. & Messinger, D. W. Virtual cleaning of works of art using a deep generative network: spectral reflectance estimation. Heritage Sci. 11, 16 (2023).

    Article  Google Scholar 

  27. Palomero, C. M. T. & Soriano, M. N. Digital cleaning and “dirt” layer visualization of an oil painting. Opt. Express 19, 21011–21017 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  28. Munoz-Pandiella, I., Andujar, C., Cayuela, B., Pueyo, X. & Bosch, C. Automated digital color restitution of mural paintings using minimal art historian input. Comput. Graphics 114, 316–325 (2023).

    Article  Google Scholar 

  29. Merizzi, F. et al. Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc. Heritage Sci. 12, 41 (2024).

    Article  Google Scholar 

  30. Priego, E., Herráez, J., Denia, J. L. & Navarro, P. Technical study for restoration of mural paintings through the transfer of a photographic image to the vault of a church. J. Cult. Heritage 58, 112–121 (2022).

    Article  Google Scholar 

  31. Cricchio, C. The restoration of the panel painting depicting the Adoration of Shepherds with a Saint Bishop. CeROArt. Conservation, exposition, Restauration d’Objets d’Art https://doi.org/10.4000/ceroart.5224 (2017).

    Article  Google Scholar 

  32. Nocheseda, C. J. C., Santos, M. F. A., Espera, A. H. & Advincula, R. C. 3D digital manufacturing technologies, materials, and artificial intelligence in art. MRS Commun. 13, 1102–1118 (2023).

    Article  ADS  CAS  Google Scholar 

  33. Elkhuizen, W. et al. Gloss, color, and topography scanning for reproducing a painting’s appearance using 3D printing. J. Comput. Cult. Heritage 12, 27:1–27:22 (2019).

    Google Scholar 

  34. Dardes, K. & Rothe, A. (eds.) The Structural Conservation of Panel Paintings: Proceedings of a Symposium at the J. Paul Getty Museum (Getty Publications, 1998).

  35. Mecklenburg, M. F., Charola, A. E. & Koestler, R. J. (eds.) New Insights into the Cleaning of Paintings: Proceedings from the Cleaning 2010 International Conference (Smithsonian Institution Scholarly Press, 2019).

  36. Yoo, W. S., Kang, K., Kim, J. G. & Yoo, Y. Extraction of color information and visualization of color differences between digital images through pixel-by-pixel color-difference mapping. Heritage 5, 3923 (2022).

    Article  Google Scholar 

  37. Antropov, S. & Bratasz, Ł. Development of craquelure patterns in paintings on panels. Heritage Sci. 12, 89 (2024).

    Article  Google Scholar 

  38. Karianakis, N. & Maragos, P. An integrated system for digital restoration of prehistoric Theran wall paintings. In 2013 18th International Conference on Digital Signal Processing (DSP) 1–6 (IEEE, 2013).

  39. Ridderbos, B., van Buren, A. & van Veen, H. T. Early Netherlandish Paintings: Rediscovery, Reception, and Research (Getty Publications, 2005).

    Google Scholar 

  40. Crowe, J. The Early Flemish Painters: Notices of Their Lives and Works (John Murray, 1872).

    Google Scholar 

  41. Hand, J. O. & Wolff, M. Early Netherlandish Painting (National Gallery of Art, 1986).

    Google Scholar 

  42. de Loo, G. H. Hans Memlinc in Rogier van der Weyden’s Studio. Burlington Magazine for Connoisseurs 52, 160–177 (1928).

    Google Scholar 

  43. Cohen, E. J., Bravi, R., Bagni, M. A. & Minciacchi, D. Precision in drawing and tracing tasks: different measures for different aspects of fine motor control. Hum. Mov. Sci. 61, 177–188 (2018).

    Article  PubMed  Google Scholar 

  44. Komarova, N. L. & Jameson, K. A. A quantitative theory of human color choices. PLoS ONE 8, e55986 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  45. Emery, K. J. & Webster, M. A. Individual differences and their implications for color perception. Curr. Opin. Behav. Sci. 30, 28–33 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Smet, K. A. G., Webster, M. A. & Whitehead, L. A. A simple principled approach for modeling and understanding uniform color metrics. J. Opt. Soc. Am. A Opt. Image. Sci. Vis. 33, A319–A331 (2016).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  47. Abasi, S., Amani Tehran, M. & Fairchild, M. D. Distance metrics for very large color differences. Color Res. Appl. 45, 208–223 (2020).

    Article  Google Scholar 

  48. Song, A., Faugeras, O. & Veltz, R. A neural field model for color perception unifying assimilation and contrast. PLoS Comput. Biol. 15, e1007050 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  49. Pazzaglia, M. et al. Loss and beauty: how experts and novices judge paintings with lacunae. Psychol. Res. 85, 1838–1847 (2021).

    Article  PubMed  Google Scholar 

  50. Saunders, D. Ultra-violet filters for artificial light sources. Tech. Bull. 13, 61–68 (1989).

    Google Scholar 

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