Open Access
Issue
MATEC Web Conf.
Volume 395, 2024
2023 2nd International Conference on Physics, Computing and Mathematical (ICPCM2023)
Article Number 01053
Number of page(s) 8
DOI https://doi.org/10.1051/matecconf/202439501053
Published online 15 May 2024
  1. Cao J., Zhang Z., Zhao A., et al. Ancient mural restoration based on a modified generative adversarial network[J]. Heritage Science, 2020, 8(1): 1–14. [Google Scholar]
  2. Li J., Wang H., Deng Z., et al. Restoration of non-structural damaged murals in Shenzhen Bao’an based on a generator-discriminator network[J]. Heritage Science, 2021, 9(1): 1–14. [Google Scholar]
  3. Zou Z., Zhao P., Zhao X. Virtual restoration of the colored paintings on weathered beams in the Forbidden City using multiple deep learning algorithms[J]. Advanced Engineering Informatics, 2021, 50: 101421. [Google Scholar]
  4. Wang N., Wang W., Hu W., et al. Damage Sensitive and Original Restoration Driven Thanka Mural Inpainting[C]//Pattern Recognition and Computer Vision: Third Chinese Conference, PRCV 2020, Nanjing, China, October 16-18, 2020, Proceedings, Part I 3. Springer International Publishing, 2020: 142-154. [Google Scholar]
  5. Zhu X., Yu Y., Deng X., et al. Bring Ancient Murals Back to Life[C]//International Conference on Neural Information Processing. Cham: Springer International Publishing, 2022: 231-242. [Google Scholar]
  6. Cao J., Zhang Z., Zhao A. Application of a modified generative adversarial network in the superresolution reconstruction of ancient murals[J]. Computational Intelligence and Neuroscience, 2020, 2020. [Google Scholar]
  7. Wang J., Zhang E., Cui S., et al. GGD-GAN: Gradient-Guided dual-Branch adversarial networks for relic sketch generation[J]. Pattern Recognition, 2023, 141: 109586. [Google Scholar]
  8. Rakhimol V., Maheswari P. U. Restoration of ancient temple murals using cGAN and PConv networks[J]. Computers & Graphics, 2022, 109: 100-110. [Google Scholar]
  9. Wang Q., Hou M., Lyu S. Virtual Restoration of Missing Paint Loss of Mural Based on Generative Adversarial Network[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021, 46: 807–811. [Google Scholar]
  10. Mittal A., Soundararajan R., Bovik A. C. Making a “completely blind” image quality analyzer[J]. IEEE Signal processing letters, 2012, 20(3): 209–212. [Google Scholar]
  11. Cao J., Yan M., Chen H., et al. Dynasty recognition algorithm of an adaptive enhancement capsule network for ancient mural images[J]. Heritage Science, 2021, 9: 1–15. [Google Scholar]
  12. Cao J., Zhang Z., Zhao A., et al. Ancient mural restoration based on a modified generative adversarial network[J]. Heritage Science, 2020, 8(1): 1–14. [Google Scholar]
  13. Yu T., Zhang S., Lin C., et al. Dunhuang grottoes painting dataset and benchmark[J]. arXiv preprint arXiv:1907.04589, 2019. [Google Scholar]

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