Safdar Muhammad, Emmel Patrick
J Opt Soc Am A Opt Image Sci Vis. 2022 Jun 1;39(6):1066-1075. doi: 10.1364/JOSAA.451931.
In learning-based reflectance reconstruction methods, usually localized training samples are used to reconstruct spectral curves. The state-of-the-art methods localize the training samples based on their colorimetric color differences with the test sample. This approach is dependent on the working color space, color difference equation, and/or illuminant used, and it may result in a metameric match. This issue can be resolved by localizing the training samples based on their spectral difference with the test sample; however, this would require an already unknown spectral curve of the test sample. In this paper, use of corresponding color information to emulate the spectral neighborhood of the test color for non-metameric reflectance recovery is proposed. The Wiener estimation method was extended by (1) using two thresholds, (i) on the color difference between the test sample and the training samples under the reference illuminant and (ii) on the color difference between the corresponding color of the test sample and the training samples under another illuminant, to mimic the spectral neighborhood of the test sample within the gamut of the training data, and (2) also using the tristimulus values of the corresponding color in the regression. Results showed that the proposed extension of the Wiener estimation method improved the reflectance recovery and hence reduced the metamerism.
在基于学习的反射率重建方法中，通常使用局部训练样本重建光谱曲线。目前的先进方法基于训练样本与测试样本的色度色差来定位训练样本。这种方法依赖于所使用的工作颜色空间、色差公式和/或光源，并且可能导致同色异谱匹配。通过基于训练样本与测试样本的光谱差异来定位训练样本，可以解决这个问题；然而，这需要测试样本已经未知的光谱曲线。本文提出利用相应颜色信息来模拟测试颜色的光谱邻域，以实现非同色异谱反射率恢复。通过以下方式扩展了维纳估计方法：（1）使用两个阈值，（i）基于参考光源下测试样本与训练样本之间的色差，以及（ii）基于另一个光源下测试样本的相应颜色与训练样本之间的色差，来在训练数据的色域内模拟测试样本的光谱邻域；（2）在回归中还使用相应颜色的三刺激值。结果表明，所提出的维纳估计方法扩展改进了反射率恢复，从而减少了同色异谱现象。
