摘要
烟熏病害是敦煌壁画的典型病害之一,具有颜色破坏大、修复难度高、相关研究少等难点,实体修复对烟熏病害尚没有有效的复原手段。针对敦煌壁画所面临的烟熏病害,以莫高窟第156窟北壁烟熏壁画作为典型研究对象,从数字化复原和物理化学分析的角度出发,利用两次烟熏模拟实验,探究了颜料在烟熏条件下产生的变化规律,收集了颜色在烟熏过程中变化的数据集,提出了基于机器学习的烟熏壁画数字化复原方法。本研究创新性地采用了基于模拟实验的数字化色彩复原研究方法,取得了珍贵的烟熏壁画数据样本,相关数字化研究成果可以应用在烟熏类壁画的数字化色彩复原中,将烟熏前的颜色展现给社会大众。
Soot damage is one of the typical types of deterioration seen in Dunhuang murals,though it is a somewhat a typical research topic when compared with other types of deterioration.Soot damage is particularly damaging to the color of murals and cannot be ameliorated by conventional physical restoration methods.Focusing on the soot damaged murals in Mogao cave156,and by conducting two simulation experiments of soot damage from the perspective of digital restoration and physicochemical analysis,this paper explores the pattern of changes in paint pigments under the influence of soot and collects relevant data on color change.The researchers then pose a digital restoration method for soot damaged murals based on machine learning that is applicable to the restoration of similarly colored murals.
作者
付心仪
李岩
孙志军
杜鹃
王凤平
徐迎庆
FU Xinyi;LI Yan;SUN Zhijun;DU Juan;WANG Fengping;XU Yingqing(Academy of Art and Design,Tsinghua University,Beijing 100084;Future Laboratory,Tsinghua University,Beijing 100084;Department of Applied Physics,University of Science and Technology Beijing,Beijing 100083;Dunhuang Academy,Dunhuang,Gansu 736200)
出处
《敦煌研究》
CSSCI
北大核心
2021年第1期137-147,共11页
Dunhuang Research
基金
国家重点研发计划(2020YFC1523001)
清华大学自主科研计划(20197010003)
关键词
烟熏壁画
数字化复原
机器学习
物理化学
模拟实验
soot damaged murals
digital restoration
machine learning
physical chemistry
simulation experiment
