Metamer mismatching occurs in both the human visual system and digital imaging systems. Increasing the number of sensors in digital imaging systems is an efficient method to reduce the degree of metamer mismatching. This study investigates the number of sensors that are needed for the digital imaging systems to have a similar ability to the human sensor system in distinguishing colors from the perspective of metamer mismatching. Optimal spectral reflectance was generated, and a dataset with more than 47 million previously collected practical spectral reflectance functions was used to derive the metameric color pairs. Different sets of Gaussian‐shaped functions were designed to model the spectral sensitivity functions of the digital imaging systems. Both the metamer mismatching volumes and color differences were used to characterize the degree of metamer mismatching for pairs of samples, which were metameric to the imaging systems but appeared different to the human sensor system. The results show that the practical metamer mismatching volumes are substantially smaller than the theoretical ones. The results also show that both the metamer mismatching volumes and color differences are significantly reduced by increasing the number of sensors from three to five for the digital imaging system but are only slightly reduced by further increasing the number of sensors from five to seven. This indicates that five sensors is an efficient and optimal solution for an imaging system with Gaussian‐shaped sensors to have a similar ability in distinguishing colors compared to the human sensor system.

中文翻译：

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从异构体错配的角度来看，用于数字成像系统的传感器的最佳数量

在人类视觉系统和数字成像系统中都发生同聚物的错配。在数字成像系统中增加传感器的数量是减少同聚物异构体失配程度的有效方法。这项研究调查了数字成像系统从同位异构体错配的角度区分颜色时，具有与人类传感器系统相似的能力所需的传感器数量。产生了最佳的光谱反射率，并使用一个具有超过4700万先前收集的实际光谱反射率函数的数据集来导出同色异谱颜色对。设计了不同的高斯型函数集来对数字成像系统的光谱灵敏度函数进行建模。同聚物的错配体积和颜色差异均用于表征成对样品的同聚物的错配程度，所述样品对成像系统是同聚的，但看起来与人类传感器系统不同。结果表明，实际的同聚物的错配体积明显小于理论值。结果还表明，通过将数字成像系统的传感器数量从三个增加到五个，显着降低了同聚物的错配体积和色差，但通过将传感器数量从五个增加到七个仅稍微降低了。这表明对于具有高斯形传感器的成像系统，与人类传感器系统相比，五个传感器是一种有效且最佳的解决方案，具有区分颜色的能力。