Shen Hui-Liang, Cai Pu-Qing, Shao Si-Jie, Xin John H
Opt Express. 2007 Nov 12;15(23):15545-54. doi: 10.1364/oe.15.015545.
In multispectral imaging, Wiener estimation is widely adopted for the reconstruction of spectral reflectance. We propose an improved reflectance reconstruction method by adaptively selecting training samples for the autocorrelation matrix calculation in Wiener estimation, without a prior knowledge of the spectral information of the samples being imaged. The performance of the proposed adaptive Wiener estimation and the traditional method are compared in the cases of different channel numbers and noise levels. Experimental results show that the proposed method outperforms the traditional method in terms of both spectral and colorimetric prediction errors when the imaging channel number is 7 or less. When the imaging system consists of 11 or more channels, the color accuracy of the proposed method is slightly better than or becomes close to that of the traditional method.
在多光谱成像中，维纳估计被广泛用于光谱反射率的重建。我们提出了一种改进的反射率重建方法，该方法在维纳估计中通过自适应选择训练样本进行自相关矩阵计算，而无需事先了解所成像样本的光谱信息。在不同通道数和噪声水平的情况下，对所提出的自适应维纳估计和传统方法的性能进行了比较。实验结果表明，当成像通道数为7或更少时，所提出的方法在光谱和比色预测误差方面均优于传统方法。当成像系统由11个或更多通道组成时，所提出方法的颜色精度略优于或接近传统方法。
