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<p>This study aims to address two major challenges in the digital restoration of murals: defect segmentation and large-scale defect repair. We propose a novel solution, combining the SAM-Adapter segmentation method with semi-supervised training and a Text-to-Inpainting strategy. In the aspect of defect segmentation, by introducing thresholding and semi-supervised training, we effectively enhanced the precision and applicability in handling sparse datasets. For large-scale defect repair, this study innovatively applies BPO (Black-box Prompt Optimization) technology, combined with GPT4V (ision) and Stable Diffusion, to achieve precise reconstruction of murals’ complex textures and structures through text-guided methods. Through extensive experimental validation, our method has demonstrated significant effectiveness in defect segmentation and large-scale repair, providing new perspectives and tools for the digital preservation of murals, and is of great significance for the protection of cultural heritage.</p>

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Automated mural restoration via semi supervised segmentation and prompt guided diffusion inpainting

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月9日

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  • Zishan Xu,
  • Shaokai Hua,
  • Zilong Wang,
  • Wei Chen,
  • Jueting Liu,
  • Tingting Xu,
  • Zehua Wang

摘要

This study aims to address two major challenges in the digital restoration of murals: defect segmentation and large-scale defect repair. We propose a novel solution, combining the SAM-Adapter segmentation method with semi-supervised training and a Text-to-Inpainting strategy. In the aspect of defect segmentation, by introducing thresholding and semi-supervised training, we effectively enhanced the precision and applicability in handling sparse datasets. For large-scale defect repair, this study innovatively applies BPO (Black-box Prompt Optimization) technology, combined with GPT4V (ision) and Stable Diffusion, to achieve precise reconstruction of murals’ complex textures and structures through text-guided methods. Through extensive experimental validation, our method has demonstrated significant effectiveness in defect segmentation and large-scale repair, providing new perspectives and tools for the digital preservation of murals, and is of great significance for the protection of cultural heritage.