基于光谱表示和独立成分分析的混合颜料分离方法
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摘要
提出一种基于光谱表示和独立成分分析的混合颜料分离方法。首先,采用光谱仪获取混合颜料光谱信息,并将其表示为离散信号的形式;然后,对信号进行独立成分分析,得到基本颜料光谱曲线,从而确定基本颜料种类。采用蒙赛尔色卡光谱制作模拟数据,进行三种色卡光谱混合信息的分离实验以及从八种色卡光谱中选择若干种混合后的分离实验,分离出的基本颜料光谱形态与已知的原始颜料光谱形态极其相似,平均相似比为97.64%,最大相似比可以达到99.95%。实验结果表明,该方法能够准确确定基本颜料种类,具有可追溯性,适用于混合颜料分离。
This paper proposed a separation method of mixed pigments based on spectrum expression and independent component analysis. First,the spectrometer was used to obtain the spectral information of mixed pigments that was expressed as the discrete signal. Second,the independent component analysis was used to calculate the type of basic pigment. The simulated spectrum data of Munsell color card was used to validate this method. This algorithm could separate the mixed pigment spectrums from three basic pigment spectrums and the mixture from several pigment spectrums within the set of eight basic pigment spectrums successfully. The curve of separated pigment spectrum was very similar to the curve of original pigment spectrum.The average similarity is 97. 64%,and the maximum one can reach 99. 95%. The experiment results show that this method can determine the composition of mixed pigments accurately,so it is suitable for separation of mixed pigments.
This paper proposed a separation method of mixed pigments based on spectrum expression and independent component analysis. First,the spectrometer was used to obtain the spectral information of mixed pigments that was expressed as the discrete signal. Second,the independent component analysis was used to calculate the type of basic pigment. The simulated spectrum data of Munsell color card was used to validate this method. This algorithm could separate the mixed pigment spectrums from three basic pigment spectrums and the mixture from several pigment spectrums within the set of eight basic pigment spectrums successfully. The curve of separated pigment spectrum was very similar to the curve of original pigment spectrum.The average similarity is 97. 64%,and the maximum one can reach 99. 95%. The experiment results show that this method can determine the composition of mixed pigments accurately,so it is suitable for separation of mixed pigments.
引文
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[2]张胜平.基于多光谱图像的古画颜色修复方法研究[D].天津:天津大学,2010.
[3]HYVARINEN A,OJA E.A fast fixed-point algorithm for independent component analysis[J].Neural Computation,1997,9(7):1483-1492.
[4]BERNS R S.颜色技术原理[M].李小梅,马如,陈立荣,译.北京:化学工业出版社,2002.
[5]张小燕.基于ICA的光谱估计方法研究[D].昆明:云南师范大学,2006.
[6]CLAUDIO O,MAURA P.Grassmann's laws and individual colormatching functions for non-spectral primaries evaluated by maximum saturation technique in foveal vision[J].Color Research and Application,2008,33(4):271-281.
[7]陆惠民.视觉系统的工程模拟与视觉理论的新进展[J].生物化学与生物物理进展,1981,8(1):23-27.
[8]MARK S,MARK F.Evaluating the 1931 CIE color-matching functions[J].Color Research and Application,2002,27(5):316-329.
[9]LI Y,STANLEY J M.Revised Kubelka-Munk theory III:a general theory of light propagation in scattering and absorptive media[J].Journal of the Optical Society of America A,2005,22(9):1866-1873.
[10]张贤达,保铮.盲信号分离[J].电子学报,2001,29(12A):1766-1771.
[11]ASANO F,IKEDA S,OGAWA M,et al.Combined approach of array processing and independent component analysis for blind separation of acoustic signals[J].IEEE Trans on Speech and Audio Processing,2003,11(3):204-215.
[12]LIU R W,INOUYE Y.Blind equalization of MIMO-FIR channels driven by white but higher order colored source signals[J].IEEE Trans on Information Theory,2002,48(5):1206-1214.
[13]VIGARIO R,SARELA J,JOUSMIKI V,et al.Independent component approach to the analysis of EEG and MEG recordings[J].IEEE Trans on Biomedical Engineering,2000,47(5):589-593.
[14]刘喜武,高伟,张宁,等.基于带状混合矩阵ICA实现地震盲反褶积[J].地球物理学进展,2007,22(4):1153-1163.
[15]林秋华.基于盲源分离的图像与语音加密新方法研究[D].大连:大连理工大学,2006.
[16]李广彪,张剑云,毛云祥.盲源分离的发展及研究现状[J].航天电子对抗,2004,23(6):13-16,23.
[17]SAPATNEKAR S S.Overcoming variations in nanometer-scale technologies[J].IEEE Journal on Emerging and Selected Topics in Circuits and Systems,2011,1(1):5-18.
[18]GILLESPIE A R,KAHLE A B,WALKER R E.Color enhancement of highly correlated images I:decorrelation and HSI contrast stretches[J].Remote Sensing of Environment,1986,20(3):209-235.
[19]ALSBERG B K,WOODWARD A M,KELL D B.An introduction to wavelet transforms for chemometricians:a time-frequency approach[J].Chemometrics and Intelligent Laboratory Systems,1997,37(2):215-239.
[20]JAMES W L,ROGER D L.Accuracy of Gram-Schmidt orthogonalization and Householder transformation for the solution of linear least squares problems[J].Numerical Linear Algebra with Applications,1998,4(4):295-303.
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