基于BP神经网络和主元分析法的数码相机光谱重构算法

Spectral Reconstruction Algorithm of Digital Camera Based on BP Neural Network and Principal Component Analysis

从数码相机的RGB信号重构物体表面的光谱反射率是光谱颜色管理研究中的重要课题之一。提出了一种基于误差反向传播前馈神经网络(BP)和主元分析法(PCA)实现色卡的表面光谱反射率重构的新算法。通过对三种色卡进行光谱重构实验研究了BP神经网络的最优结构和主元数的最佳选择,验证了算法的精度。实验结果表明,采用适当的BP神经网络和主元分析相结合的新算法能够精确重构同类色卡的表面光谱反射率。

Reconstructing the spectral reflectence of the object surface from RGB signals of digital camera is one of the important studies of spectral color managament. A new algorithm based on back propagation （BP） neural network and principal component analysis （PCA） is proposed to realize the spectral reflectence reconstruction of color atlas. The optimal structure of BP neural network and the number of principal components are studied in the spectral reflectence reconstruction experiments of three color atlases and the accuracy of the algorithm is also testified. The experimental results show that the new algorithm of appropriate BP neural network combined with PCA is satisfied to accurately reconstruct the spectral reflectence of the same kind of color atlas.