State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.
Opt Lett. 2013 Aug 1;38(15):2818-20. doi: 10.1364/OL.38.002818.
Spectral reflectance is defined as the "fingerprint" of an object and is illumination invariant. It has many applications in color reproduction, imaging, computer vision, and computer graphics. In previous reflectance reconstruction methods, spectral reflectance has been treated equally over the whole wavelength. However, human eyes or sensors in an imaging device usually have different weights over different wavelengths. We propose a novel method to reconstruct reflectance, considering a wavelength-sensitive function (WSF) that is constructed from sensor-sensitive functions (or color matching functions). Our main idea is to achieve more accurate reconstruction at wavelengths where sensors have high sensitivities. This more accurate reconstruction can achieve better imaging or color reproduction performance. In our method, we generate a matrix through the Hadamard product of the reflectance matrix and the WSF matrix. We then obtain reconstructed reflectance by applying the singular value decomposition on the generated matrix. The experimental results show that our method can reduce 47% mean-square error and 55% Lab error compared with the classical principal component analysis method.
光谱反射率被定义为物体的“指纹”，且与照明无关。它在颜色再现、成像、计算机视觉和计算机图形学等领域有许多应用。在之前的反射率重建方法中，整个波长范围内的光谱反射率被平等对待。然而，人眼或成像设备中的传感器通常对不同波长有不同的权重。我们提出了一种新的方法来重建反射率，考虑了一种由传感器敏感函数（或颜色匹配函数）构造的波长敏感函数（WSF）。我们的主要思想是在传感器具有高灵敏度的波长处实现更准确的重建。这种更准确的重建可以实现更好的成像或颜色再现性能。在我们的方法中，我们通过反射率矩阵和 WSF 矩阵的 Hadamard 乘积生成一个矩阵。然后，我们通过对生成的矩阵应用奇异值分解来获得重建的反射率。实验结果表明，与经典的主成分分析方法相比，我们的方法可以减少 47%的均方误差和 55%的 Lab 误差。
