Revealing Disappearance Patterns in Murals Heavily Contaminated by Soot Based on Hyperspectral Imaging.

Over time, many murals have become heavily contaminated with soot, obscuring patterns and hindering the recognition of these significant cultural relics. This study utilises the spectral discrimination and subsurface detection capabilities of hyperspectral imaging to nondestructively reveal pattern...

Descripción completa

Detalles Bibliográficos
Publicado en:Archaeometry Vol. 68; no. 3; pp. 527 - 540
Autores principales: Sun, Pengyu, Hou, Miaole, Lyu, Shuqiang, Cui, Wenyi, Wang, Wanfu, Sun, Yutong
Formato: Artículo
Publicado: Wiley-Blackwell Jun2026
Materias:
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=193599604&site=ehost-live
header:
  @attributes:
    shortDbName: hlh
    uiTerm: 193599604
    longDbName: Humanities International Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        0003813X
        D7X
      jtl: Archaeometry
      issn: 0003813X
      maglogo: Y
    pubinfo:
      dt: Jun2026
      vid: 68
      iid: 3
      pid: 480
      pub: Wiley-Blackwell
    artinfo:
      ui:
        193599604
        10.1111/arcm.70076
      ppf: 527
      ppct: 13
      formats:
      tig:
        atl: Revealing Disappearance Patterns in Murals Heavily Contaminated by Soot Based on Hyperspectral Imaging.
      aug:
        au:
          Sun, Pengyu
          Hou, Miaole
          Lyu, Shuqiang
          Cui, Wenyi
          Wang, Wanfu
          Sun, Yutong
        affil:
          Beijing University of Civil Engineering and Architecture, Beijing, China
          Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, Beijing, China
          The Dunhuang Academy, Dunhuang, China
          China Academy of Cultural Heritage, Beijing, China
      su:
        Hyperspectral imaging systems
        Art conservation & restoration
        Image enhancement (Imaging systems)
        Pollution
        Pattern perception
        Independent component analysis
        Cultural property
        Archaeology
      sug:
        subj:
          Hyperspectral imaging systems
          Art conservation & restoration
          Image enhancement (Imaging systems)
          Pollution
          Pattern perception
          Independent component analysis
          Cultural property
          Archaeology
      keyword:
        EMP‐PCA
        hidden information
        hyperspectral imaging
        sooty murals
        weight map
      ab: Over time, many murals have become heavily contaminated with soot, obscuring patterns and hindering the recognition of these significant cultural relics. This study utilises the spectral discrimination and subsurface detection capabilities of hyperspectral imaging to nondestructively reveal pattern information concealed by soot. After preprocessing the hyperspectral data, independent component analysis (ICA) is used to preliminarily separate soot from the underlying patterns. Several independent components containing the most distinct pattern information are selected for a two‐part enhancement strategy. The first part introduces a novel feature extraction method, EMP‐PCA, to highlight and extract the patterns, followed by contrast stretching to enhance the pattern–background distinction. The second part uses an inverse ICA transformation on selected components to synthesise a true colour image, which is then enhanced using a weight map–based method. Finally, the results from both parts are combined using Laplacian fusion, comprehensively revealing the patterns hidden beneath the heavy soot. The results demonstrate the method's effectiveness and provide valuable information for the archaeological study and conservation of cultural heritage.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: Y
      dt:
        @attributes:
          year: 2026
    holdings:
      @attributes:
        islocal: N