摘要
矿物颜料作为我国古代彩绘所用的主要颜料,部分颜色经过岁月洗礼发生褪变色现象,其色彩复原已成为研究的重点之一,矿物颜料的识别和分布映射是色彩复原的重要依据。本研究首先阐述了矿物颜料的光学特性,并构建了土红、雌黄等矿物颜料的吸收散射数据库;其次,基于人眼的视觉感知特性,构建修正的双常数Kubelka-Munk模型以获得色块的浓度分布映射图谱;最后,通过吸收散射数据库计算出各匹配色的光谱反射率以及色度值,以光谱匹配度和CIEDE2000色差来验证模型的精度。实验数据表明,本研究所提出的模型能够较好表征色块的浓度值,模型预测浓度误差的平均值为1.838%。该方法能够较好地反映矿物颜料的浓度分布情况,对古代彩绘的计算机复原有一定的参考价值。
Mineral pigments is the main pigments used in ancient painting in China.Some of the color faded over the years.The color reconstruction has become one of the key points of research,and the identification and distribution mapping of mineral pigments are important bases for color reconstruction.Firstly,optical properties of mineral pigments were described,and the absorbing and scattering database of mineral pigments was constructed such as red clay and orpiment in this study.Secondly,a modified two-constant Kubelka-Munk model based on the visual perceptual properties of human eyes was constructed to obtain the concentration distribution mapping profiles of color blocks.Finally,the spectral reflectance of each matching color and the chromaticity values were calculated from the absorbing and scattering database to verify the accuracy of the model in terms of spectral matching degree and CIEDE2000 color difference.The experimental data showed that the proposed model can better characterize the concentration values of color blocks,and the mean value of the model prediction concentration error was 1.838%.The method can better reflect the concentration distribution of mineral pigments and has certain reference value for computer reconstruction of ancient painting.
作者
樊硕
胡霄鹏
刘振
冯漪
赵安然
刘莉
刘思鲁
FAN Shuo;HU Xiao-peng;LIU Zhen;FENG Yi;ZHAO An-ran;LIU Li;LIU Si-lu(College of Engineering,Qufu Normal University,Rizhao 276800,China;Changge Dayang Paper Industry Co.,Ltd.,Changge 461500,China;School of Communication,Qufu Normal University,Rizhao 276800,China;Ulanhot Senhui Printing Co.,Ltd.,Ulanhot 137400,China)
出处
《印刷与数字媒体技术研究》
CAS
北大核心
2023年第5期17-25,共9页
Printing and Digital Media Technology Study
基金
国家自然科学基金(No.61405104)
山东省自然科学基金(No.ZR2020MF125)。