Shen Hui-liang, Zhang Zhe-chao, Xin John H
Department of Information and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Apr;29(4):1050-5.
Spectral reflectance reconstruction, also referred to as spectral characterization, aims to recover accurate spectral reflectance of object surface by employing standard color charts. As there are always a large number of color samples on a color chart, spectral characterization becomes a time-consuming process for practical application. Some methods have been presented to selected representative color samples based on the redundancy of the colors on a chart. However, these methods only consider the distribution of spectral reflectance, and thus the selected colors may not be optimal for a specific imaging system. To deal with this problem, the present paper proposes a sequential method for the selection of most representative colors, which consists of two steps. In the first step, a part of representative colors are selected according to the minimization of mean spectral root-mean-square error, by assuming a virtual imaging system. The spectral responsivity of the real imaging system is then calculated based on these selected samples. In the second step, additional representative colors are selected based on the characteristics of the real imaging system. Two quite different systems, i. e. , an 11-channel narrowband multispectral imaging system and a 3-channel broadband color scanner, were used in the experiment. It was shown that the proposed method significantly outperforms the previous method in terms of both spectral and colorimetric accuracy.
光谱反射率重建，也称为光谱表征，旨在通过使用标准色卡来恢复物体表面的准确光谱反射率。由于色卡上总是存在大量颜色样本，光谱表征在实际应用中成为一个耗时的过程。已经提出了一些方法来基于色卡上颜色的冗余性选择代表性颜色样本。然而，这些方法仅考虑光谱反射率的分布，因此所选颜色对于特定成像系统可能不是最优的。为了解决这个问题，本文提出了一种用于选择最具代表性颜色的顺序方法，该方法包括两个步骤。第一步，通过假设一个虚拟成像系统，根据平均光谱均方根误差的最小化来选择一部分代表性颜色。然后基于这些所选样本计算真实成像系统的光谱响应度。第二步，根据真实成像系统的特性选择额外的代表性颜色。实验中使用了两个截然不同的系统，即一个11通道窄带多光谱成像系统和一个3通道宽带彩色扫描仪。结果表明，所提出的方法在光谱和色度准确性方面均明显优于先前的方法。
