基于最优化维纳估计算法的多光谱图像重构

目的提高光谱反射率重构精度,更好实现图像颜色再现。方法选择4种不同的训练样本,比较不同的改进的维纳估计算法重构光谱反射率精度。分别把Color Checker Color Rendition Chart(RC-24色)和Color Checker SG(140色)以及Panton色卡、孟塞尔色卡(Munsell-1269)4种不同颜色数量的哑光色卡作为训练样本,用伪逆算法、维纳估计算法、自适应维纳估计算法和最优化维纳估计算法分别对多光谱图像进行重构,并进行精度对比。从均方根误差、色差等方面评价这些光谱重构方法。结果最优化维纳估计算法重构均方根误差为0.000 41,色差为0.7874,均小于其他几种算法。结论最优化维纳估计算法能更好实现颜色再现。

Multispectral Image Reconstruction Based on Optimized Wiener Estimation

The aim of this study was to improve the spectral reflectance reconstruction precision, and to better realize the reproduction of the image color. Four different kinds of training samples were selected and the reconstruction precision of spectral reflectance of different improved Wiener estimation algorithms was compared. Four matte color cards with different color numbers such as Color Checker Color Rendition Chart, Color Checker SG, copperplate paper spot color card of Panton, and Munsell color card were chosen as training samples, and to reconstruct spectral image by the algorithms of pseudo inverse, Wiener, adaptive Wiener estimation and optimized Wiener estimation, and the results were compared. These methods of spectral reconstruction were evaluated by the root mean square error and color difference accuracy. The results showed that the reconstruction root mean square error (RMSE) based on optimized Wiener estimation was 0.00041 and the color difference was 0.7874 which were both lower than those of other kinds of algorithms. So it could be concluded that the algorithm of optimized Wiener estimation could better realize color reproduction.