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光谱Neugebauer模型的抗差最小二乘优化

Robustified Least Squares for Spectrum Neugebauer Model
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摘要 Yule-Nielsen修正光谱Neugebauer模型是一种用来预测彩色网目调印刷品颜色的重要光谱预测模型。为了提高Neugebauer模型预测精度,在分析光谱Neugebauer模型和抗差估计理论的基础上,提出了将抗差最小二乘法用于计算模型参数网点面积率和基色光谱反射率。常用的抗差估计方案有:Huber估计和IGG估计。实验表明,当存在粗差的情况下,与传统的最小二乘法相比,抗差最小二乘法的光谱Neugebauer模型的预测精度更高且更为稳定。 Yule-Nielsen modified spectrum Neugebauer model is an important spectrum prediction model in predicting color output of color halftone prints.In order to improve the prediction accuracy,spectrum Neugebauer model and robustified estimation theory were analyzed and a method of robustified least squares to estimate dot area function and primary spectrum reflectance were put forward.Commonly used robust estimation programs are Huber estimation and IGG estimation.Experiment results showed that compared to traditional least squares(LS) based methods,the accuracy of spectrum Neugebauer model with RLS approach is higher and more stable under the condition of gross error.
机构地区 江南大学
出处 《包装工程》 CAS CSCD 北大核心 2011年第21期106-109,共4页 Packaging Engineering
关键词 Neugebauer模型 抗差最小二乘法 色彩管理 Neugebauer model robustified least square color management
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参考文献9

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