用多隐层BP网实现的CRT色度变换

为了实现彩色信息的标准化显示，需要对ＣＲＴ的色度空间进行标定，也就是ＣＲＴ的Ｒ、Ｇ、Ｂ空间与ＣＩＥ的标准色度空间的相互转换问题，人工神经网络近年来被广泛应用于多种颜色空间的变换过程中，在分析前人经验的基础上，提出了一种采脾四隐层ＢＰ网的ＣＲＴ的Ｒ、Ｇ、Ｂ与ＣＩＥ的Ｘ、Ｙ、Ｚ色度空间的变换方法，实验结果表明，该方法的收敛性和训练时间均优于前人采用２个或３个隐层的方案，而且通过对５１２个训练样本的实验

CRT Color Conversion by a Multi-Layer BP Neural Networks

CRTs(Cathode Ray Tubes)are major display devices in computers. In practical cases, in order to display colors on a CRT on standard, we must do color calibration work for it, which is the problem of color notation conversion between the RGB space of the CRT and the XYZ space of CIE system. Neural networks is one of methods in color notation conversion. In this paper, we proposes a BP Neural networks with four hidden layer to perform the color notation conversion from RGB space to XYZ space in a computer controlled CRT system. The experimental results show our method is better than the method which using only 2~3 hidden layers, and the color conversion precision of our experiment is about 1 5 CIELUV units.