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Nowadays, with the assistance of advanced technology, everything is constantly evolving in every sector, such as transportation, economics, medication, and so on. Also, in the sector of cultural heritage, image inpainting technology has become very popular as an effective way that allows conservators to digitally restore damaged murals without physically altering the original artwork, preserving its integrity and historical authenticity. In this paper, an image inpainting framework is proposed to restore the ancient murals of Myanmar from AD 1800–1900. The framework can be subdivided into crack removal and lacuna removal. The identification of crack and lacuna damage is automatically done with segmentation and image processing methods. Crack damage is reconstructed with pixel neighboring transfer, while lacuna reconstruction is applied with coherent transport and patch-based nearest neighbor similarity color filling methods. The accuracy is tested with the damage ratio analysis, and the experimental result demonstrates that the framework can deliver satisfactory visual results in the reconstruction process of the murals.

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Applying Digital Restoration Techniques in Preservation of Ancient Murals Using Diffusion-Based Inpainting

  • 会议论文

  • 发表日期：

    2025年

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  • Khant Khant Win Tint,
  • Mie Mie Tin,
  • Thi Thi Zin,
  • Pyke Tin

摘要

Nowadays, with the assistance of advanced technology, everything is constantly evolving in every sector, such as transportation, economics, medication, and so on. Also, in the sector of cultural heritage, image inpainting technology has become very popular as an effective way that allows conservators to digitally restore damaged murals without physically altering the original artwork, preserving its integrity and historical authenticity. In this paper, an image inpainting framework is proposed to restore the ancient murals of Myanmar from AD 1800–1900. The framework can be subdivided into crack removal and lacuna removal. The identification of crack and lacuna damage is automatically done with segmentation and image processing methods. Crack damage is reconstructed with pixel neighboring transfer, while lacuna reconstruction is applied with coherent transport and patch-based nearest neighbor similarity color filling methods. The accuracy is tested with the damage ratio analysis, and the experimental result demonstrates that the framework can deliver satisfactory visual results in the reconstruction process of the murals.