油墨组分比例预测模型与方法
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摘要
因光学特性(吸收系数与散射吸收)与组分比例不呈严格的线性关系,基于K-M理论的配色模型无法保证比例预测精度,针对上述问题,建立了油墨组分比例预测模型与方法。首先利用与组分比例具有强线性相关性的特征波长处的光谱反射率倒数值替换K-M配色理论中的吸收系数与散射系数,引入非线性项,构建油墨混合呈色模型;然后在此基础上建立油墨组分比例预测模型。以两组二元基色油墨混合样本为例,对提出的油墨组分比例预测模型及方法进行验证。实验结果表明,文中方法可预测获得与真实结果较为接近的组分比例,两组实验样本的预测平均偏差分别为1.57%和3.6%,可为目标样油墨组分比例预测提供一种新的方法。
Because the optical properties(absorption coefficient and scattering coefficient) do not have a strict linear relationship with the components proportion, the K-M based color matching model cannot guarantee the prediction precision of components proportion. In view of the problem mentioned above, this paper proposes an ink components proportion prediction model. First, an ink mixing model is established by replacing the absorption coefficient and scattering coefficient in the K-M theory with the reciprocal of spectral reflectance which has a strong linear correlation with the component proportion in certain characteristic wavelengths and introducing a additional nonlinear term. On this basis an ink components proportion prediction model is established. Taking two groups of samples mixed with binary primary inks as an example, the proposed model and method were verified. The experimental results showed that this method could predict components proportion that were close to the real results. The prediction average deviations of the two groups were 1.57% and 3.6% respectively. The proposed model and method can provide a new possibility for the ink components proportion prediction of target sample.
Because the optical properties(absorption coefficient and scattering coefficient) do not have a strict linear relationship with the components proportion, the K-M based color matching model cannot guarantee the prediction precision of components proportion. In view of the problem mentioned above, this paper proposes an ink components proportion prediction model. First, an ink mixing model is established by replacing the absorption coefficient and scattering coefficient in the K-M theory with the reciprocal of spectral reflectance which has a strong linear correlation with the component proportion in certain characteristic wavelengths and introducing a additional nonlinear term. On this basis an ink components proportion prediction model is established. Taking two groups of samples mixed with binary primary inks as an example, the proposed model and method were verified. The experimental results showed that this method could predict components proportion that were close to the real results. The prediction average deviations of the two groups were 1.57% and 3.6% respectively. The proposed model and method can provide a new possibility for the ink components proportion prediction of target sample.
引文
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[12] 林剑,孙武凤,阴金全,等.基于粒子群优化算法的肢印专色油墨配色 [J].杭州电子科技大学学报,2013,33(4):79-82.LIN J,SUN W F,YIN J Q,et al..Particle swarm optimization based spot color matching algorithm [J].J.Hangzhou Dianzi Univ.,2013,33(4):79-82.(in Chinese)
[13] 张东,塔西甫拉提·特依拜,张飞,等.分数阶微分在盐渍土高光谱数据预处理中的应用 [J].农业工程学报,2014,30(24):151-160.ZHANG D,TASHPOLAT·TIYIP,ZHANG F,et al..Application of fractional differential in preprocessing hyperspectral data of saline soil [J].Trans.Chin.Soc.Agric.Eng.,2014,30(24):151-160.(in Chinese)
[2] YANG H Y,ZHU S K,PAN N.On the Kubelka—munk single-constant/two-constant theories [J].Tex.Res.J.,2010,80(3):263-270.
[3] DAVIDSON H R,HEMMENDINGER H.Color prediction using the two-constant turbid-media theory [J].J.Opt.Soc.Am.,1966,56(8):1102-1109.
[4] BERNS R S,MOHAMMADI M.Single-constant simplification of Kubelka-Munk turbid-media theory for paint systems—a review [J].Color Res.Appl.,2007,32(3):201-207.
[5] BANDPAY M G,AMERI F,ANSARI K,et al..Mathematical and empirical evaluation of accuracy of the Kubelka-Munk model for color match prediction of opaque and translucent surface coatings [J].J.Coat.Technol.Res.,2018,15(5):1117-1131.
[6] 杨红英.织物类不透明介质中的色料混合定律 [D].上海:东华大学,2009.YANG H Y.Mixing Law of Colorants in Turbid Media Like Fabric [D].Shanghai:Donghua University,2009.(in Chinese)
[7] WEI C A,WAN X X,LI J F.A modified single-constant Kubelka-Munk model for color prediction of pre-colored fiber blends [J].Cellulose,2018,25(3):2091-2102.
[8] EMMEL P,HERSCH R D.A unified model for color prediction of halftoned prints [J].J.Imaging Sci.Technol.,2000,44(4):351-359.
[9] MCDONALD R.Colour Physics for Industry [M].2nd ed.Bradford:The Society of Dyers and Colourists,1997.
[10] 庞也驰,庞多益,赵蕾,等.基于复频谱色度理论的油墨配色技术研究 [J].中国印刷与包装研究,2009,1(2):22-25.PANG Y C,PANG D Y,ZHAO L,et al..Study of ink formulation technology based on complex frequency spectrum color theory [J].China Print.Pack.Study,2009,1(2):22-25.(in Chinese)
[11] FURFERI R,GOVERNI L,VOLPE Y.Color matching of fabric blends:hybrid Kubelka-Munk+artificial neural network based method [J].J.Electron.Imaging,2016,25(6):061402-1-11.
[12] 林剑,孙武凤,阴金全,等.基于粒子群优化算法的肢印专色油墨配色 [J].杭州电子科技大学学报,2013,33(4):79-82.LIN J,SUN W F,YIN J Q,et al..Particle swarm optimization based spot color matching algorithm [J].J.Hangzhou Dianzi Univ.,2013,33(4):79-82.(in Chinese)
[13] 张东,塔西甫拉提·特依拜,张飞,等.分数阶微分在盐渍土高光谱数据预处理中的应用 [J].农业工程学报,2014,30(24):151-160.ZHANG D,TASHPOLAT·TIYIP,ZHANG F,et al..Application of fractional differential in preprocessing hyperspectral data of saline soil [J].Trans.Chin.Soc.Agric.Eng.,2014,30(24):151-160.(in Chinese)