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<p>While epitaphs recording tomb occupants’ identities and biographies provide critical insights for archeological discoveries, their cinnabar inscriptions often suffer severe degradation during prolonged burial. To address this challenge, this study proposes a hyperspectral processing framework designed to enhance the readability of degraded cinnabar inscriptions on epitaphs. The framework quantifies sample spectral curves using Euclidean distance, enabling classification into high-contrast and low-contrast groups. For high-contrast groups (Branch 1), optimal spectral bands are selected through the optimum index factors, with subsequent integration of imaging differentials and edge detection to enhance text contrast and boundary definition. Branch 2 employs pseudo-color image synthesis, dark channel prior-based feature restoration, and bilateral filtering Retinex algorithms to achieve noise reduction and feature enhancement for low-contrast groups. Experimental results demonstrate the framework’s superiority over conventional methods in significantly improving the readability of cinnabar inscriptions, highlighting its potential as a valuable tool for archeological epigraphy.</p>

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CIEM-SCC: cinnabar inscription enhancement method via spectral curve classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月25日

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  • Wang Jun,
  • Yihao Fu,
  • Jing Yan,
  • Shenglin Peng,
  • Shuyi Qu,
  • Jinye Peng

摘要

While epitaphs recording tomb occupants’ identities and biographies provide critical insights for archeological discoveries, their cinnabar inscriptions often suffer severe degradation during prolonged burial. To address this challenge, this study proposes a hyperspectral processing framework designed to enhance the readability of degraded cinnabar inscriptions on epitaphs. The framework quantifies sample spectral curves using Euclidean distance, enabling classification into high-contrast and low-contrast groups. For high-contrast groups (Branch 1), optimal spectral bands are selected through the optimum index factors, with subsequent integration of imaging differentials and edge detection to enhance text contrast and boundary definition. Branch 2 employs pseudo-color image synthesis, dark channel prior-based feature restoration, and bilateral filtering Retinex algorithms to achieve noise reduction and feature enhancement for low-contrast groups. Experimental results demonstrate the framework’s superiority over conventional methods in significantly improving the readability of cinnabar inscriptions, highlighting its potential as a valuable tool for archeological epigraphy.