Li Chan, Wan Xiao-xia, Liu Qiang, Liang Jin-xing, Li Jun-feng
Guang Pu Xue Yu Guang Pu Fen Xi. 2016 May;36(5):1400-5.
The composition of training samples set is an important influence factor of spectral reflectance reconstruction process. Representative color samples selection for learning-based spectral reflectance reconstruction is discussed in this paper. A method based on Principal Component Analysis (PCA) is proposed to perform sample selection. First of all, a part of samples are selected according to the minimum Euclidean distance criteria in terms of camera response value from a large number of samples, which aim to ensure the similarity between training samples and target samples. Then the PCA data processing method is applied to these samples after removing the duplicate samples. The samples with larger principal component loadings are regarded as the representative color samples. Different thresholds for each principal component are used to make decision whether the loading of sample is large enough. In order to validate the proposed method, the selected samples are used as training samples to recover the spectral reflectance of color patches. A real multi-channel imaging system by loading broadband color filters in front of lens is used in the experiment to acquire the multi-channel image dataset. In this paper the pseudo-inverse method is employed to reconstruct spectral reflectance of target color patches. It is shown that the proposed method is superior to the previous methods in spectral reconstruction accuracy and can meet the requirements of high precision color reproduction.
训练样本集的组成是光谱反射率重建过程的一个重要影响因素。本文讨论了基于学习的光谱反射率重建中代表性颜色样本的选择。提出了一种基于主成分分析（PCA）的样本选择方法。首先，从大量样本中根据相机响应值的最小欧几里得距离准则选择一部分样本，目的是确保训练样本与目标样本之间的相似性。然后，在去除重复样本后，将PCA数据处理方法应用于这些样本。主成分载荷较大的样本被视为代表性颜色样本。对每个主成分使用不同的阈值来判断样本的载荷是否足够大。为了验证所提出的方法，将所选样本用作训练样本以恢复色样的光谱反射率。实验中使用了一个通过在镜头前加载宽带滤色片的真实多通道成像系统来获取多通道图像数据集。本文采用伪逆方法重建目标色样的光谱反射率。结果表明，所提出的方法在光谱重建精度上优于先前的方法，能够满足高精度颜色再现的要求。
