摘要
目的为了提升数码印花中彩色图像的复现精度,提出一种在子空间采用遗传算法优化BP神经网络的颜色特性化方法。方法介绍遗传算法(GA)优化BP神经网络的基本原理,设计一种在L^*a^*b^*颜色子空间建立的颜色特性化模型,并对1000个色样开展GA-BP神经网络模型训练实验,最终拟合出印花色样的L^*a^*b^*色度值和输入的印花图像RGB驱动值之间的非线性关系。结果该方法对125个测试色样的颜色特性化预测结果显示,超过90%的色样色差分布在2.0以内,光谱均方根误差(RMSE)分布在0.02以内。结论该方法较未进行遗传算法优化BP神经网络,预测精度得到明显提升,能够达到较高的数码喷墨印花彩色图像复现精度。
The work aims to propose a method of color characterization of a BP neural network with genetic algorithm optimization in the subspace, so as to improve the reproduction accuracy of the color image in digital printing. The basic principle of BP neural network with genetic algorithm(GA) optimization was introduced. A color characterization model established in L^*a^*b^* color subspace was designed, and a GA-BP neural network model training experiment was conducted for 1,000 color samples. The training experiment finally fitted the nonlinear relationship between the L^*a^*b^* chromaticity value of the printed color sample and the inputted RGB driving value of the printed image. The color characterization of 125 test color samples were predicted by the proposed method. The predication results showed that, more than 90% of the color differences were distributed within 2.0 and the spectral RMSE was within 0.02. Compared with the BP neural network without genetic algorithm optimization, the prediction accuracy of the proposed method is obviously improved, which can achieve high reproduction accuracy of color image in digital inkjet printing.
作者
高敏
李鹏飞
苏泽斌
杨金锴
GAO Min;LI Peng-fei;SU Ze-bin;YANG Jin-kai(College of Electrics and Information,Xi'an Polytechnic University,Xi'an 710048,China)
出处
《包装工程》
CAS
北大核心
2019年第21期235-241,共7页
Packaging Engineering
基金
陕西省教育厅科研计划（18JK0339）
陕西省重点研发计划（2019ZDLGY01-08）
