Laser & Optoelectronics Progress, Volume. 58, Issue 6, 630002(2021)

Pigment Identification and Color Analysis of Ancient Murals Based on Visible Spectroscopy

Li Miaoxin1,2, Wei Chun''ao2,3, Wan Xiaoxia2, and Li Junfeng2,3、*
Author Affiliations
  • 1School of Information Engineering, Henan University of Animal Husbandry and Economy, Zhengzhou, Henan 450046, China
  • 2Hubei Province Engineering and Technical Research Center for Digitization and Virtual Reproduction of Color Information of Culture Relics, Wuhan University, Wuhan, Hubei 430079, China
  • 3School of Packaging and Printing Engineering, Henan University of Animal Husbandry and Economy, Zhengzhou, Henan 450046, China
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    Subjective color analysis is affected by factors such as lighting sources and observers. To avoid this subjectivity, the pigments of ancient murals are identified using visible spectroscopy, and the characteristics and historical evolution of the ancient murals'' color are then analyzed objectively using the chromatic theory. We built a nondestructive acquisition system and used it to capture the visible spectra of the typical Mogao Grottoes murals painted in different dynasties. Many typical pigment samples were prepared by simulating the ancient process. The visible spectra and particle sizes were measured to construct a standard reference database from the obtained results. After analyzing the relationships among the visible spectra, material composition, particle size, and color attributes, a pigment identification method was proposed to identify the pigment used on the murals. The color attributes and historical evolution of the Mogao Grottoes murals were analyzed by comparing the identified pigments with those in the reference database.

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    Li Miaoxin, Wei Chun''ao, Wan Xiaoxia, Li Junfeng. Pigment Identification and Color Analysis of Ancient Murals Based on Visible Spectroscopy[J]. Laser & Optoelectronics Progress, 2021, 58(6): 630002

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    Paper Information

    Category: Spectroscopy

    Received: Aug. 5, 2020

    Accepted: --

    Published Online: Mar. 11, 2021

    The Author Email: Junfeng Li (ljf@hnuahe.edu.cn)

    DOI:10.3788/LOP202158.0630002

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