基于GA-BP神经网络的彩色扫描仪光谱特征化

为了实现彩色扫描仪的光谱特征化,采用一种GA修正的BP神经网络与PCA相结合的方法对其进行研究。首先,通过主成分分析,对训练样本的光谱反射率进行降维,以RGB信号和降维后的光谱数据作为输入、输出变量进行GA-BP神经网络的建模,对任意RGB信号都可以通过模型得到其低维光谱信号;再通过主成分分析重构光谱反射率,由此实现RGB信号对光谱反射率的重构,即实现扫描仪的光谱特征化。实验结果表明,GA的优化有效地改善了BP神经网络的极值问题,提高了模型的预测精度,PCA在不影响模型精度的同时提高了模型的效率。由此说明,所提出的模型能够满足扫描仪光谱特征化的需求。

Spectral Characterization of Color Scanners Based on GA-BP Neural Network

To achieve spectral characterization of color scanners, a spectral characterization model based on GA-BP and PCA was proposed. Firstly, the dimension of spectral reflectance was reduced by PCA. The GA-BP neural network model was built with input of variables of RGB signal and output of variables of low dimensional spectrum signal. Any low dimensional spectrum signal could be got by this model with any input RGB signal, while the spectral reflectance could be reconstructed by PCA. The spectral characteristics of color scanners were achieved. Experimental results show that the extremum problem of BP neural network could be effectively improved by GA. PCA could improve the operating efficiency of the model under the circumstances of maintaining accuracy. This implied it was a high-precision color scanner characte-ristic model.