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<p>Ancient murals represent invaluable heritage, providing deep insights into historic culture. However, these murals are increasingly at risk due to long-term degradation caused by oxidation and inadequate protection, and other factors, resulting in damages such as peeling and mold. Furthermore, the challenge posed by low-light conditions during image capture exacerbates the analyses and the restoration process, making it difficult to effectively identify and repair defects. To tackle these pressing challenges and facilitate efficient batch restoration at archeological sites, we propose a two-stage restoration model named MER. First, our model employs an innovative illumination enhancement module to improve the lighting of low-light mural images. Second, an automatic defect detection strategy, combined with a multi-receptive field approach, is utilized to systematically restore the identified defects. Comprehensive evaluations demonstrate that our MER model significantly enhances the visual quality of the restored images and achieves superior performance on relevant metrics compared to existing methods. Our works highlight the importance of addressing both lighting issues and defect detection in ancient mural restoration. Furthermore, we have launched a website dedicated to the restoration of ancient mural paintings, utilizing the proposed model. Code is available at <a href="https://gitee.com/bbfan2024/MER.git">https://gitee.com/bbfan2024/MER.git</a>.</p>

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Progressive enhancement and restoration for mural images under low-light and defective conditions based on multi-receptive field strategy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月6日

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  • Xiameng Wei,
  • Binbin Fan,
  • Ying Wang,
  • Yanxiang Feng,
  • Laiyi Fu

摘要

Ancient murals represent invaluable heritage, providing deep insights into historic culture. However, these murals are increasingly at risk due to long-term degradation caused by oxidation and inadequate protection, and other factors, resulting in damages such as peeling and mold. Furthermore, the challenge posed by low-light conditions during image capture exacerbates the analyses and the restoration process, making it difficult to effectively identify and repair defects. To tackle these pressing challenges and facilitate efficient batch restoration at archeological sites, we propose a two-stage restoration model named MER. First, our model employs an innovative illumination enhancement module to improve the lighting of low-light mural images. Second, an automatic defect detection strategy, combined with a multi-receptive field approach, is utilized to systematically restore the identified defects. Comprehensive evaluations demonstrate that our MER model significantly enhances the visual quality of the restored images and achieves superior performance on relevant metrics compared to existing methods. Our works highlight the importance of addressing both lighting issues and defect detection in ancient mural restoration. Furthermore, we have launched a website dedicated to the restoration of ancient mural paintings, utilizing the proposed model. Code is available at https://gitee.com/bbfan2024/MER.git.