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Geometry-Independent Target-Based Camera Colorimetric Characterization

机译：与几何无关的基于目标的相机比色特性

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摘要

An accurate colorimetric characterization of digital still cameras (DSCs) is vital to any high-quality color-reproduction system. However, achieving a perfect relationship between DSC responses and input spectral radiance is not practically easy, even when they have a reasonable linear relationship. In this research, we investigated differences in capturing geometries as a source of nonlinearity in camera characterization workflows. This nonlinearity can be corrected using a physical model describing the spectrophotometric changes according to illumination/capturing geometries. We introduced a model based on the Saunderson equation as an approach to predict surface properties suitable for paint layers in different geometries. According to the results, the Saunderson surface correction successfully compensated for the dissimilarities among spectrophotometric and spectroradiometric measurements, regardless of the capturing and lighting geometries. The model was also used for characterizing digital still cameras using matte, semi-glossy and glossy color targets as training datasets. The Saunderson-based models improved the transformation matrix tor different geometries compared to conventional methods. Also, the results confirmed the validity of a simpler derivation of the Saunderson surface correction based on linear matrix operations.
机译：数码相机（DSC）的准确色度表征对于任何高质量的色彩复制系统都是至关重要的。但是，即使在DSC响应和输入光谱辐射率之间具有合理的线性关系时，要实现它们之间的理想关系也不容易。在这项研究中，我们调查了在捕获几何结构方面的差异，这些差异是相机表征工作流程中非线性的来源。可以使用描述照度/捕获几何形状的分光光度变化的物理模型来校正此非线性。我们引入了基于Saunderson方程的模型作为预测适用于不同几何形状油漆层的表面特性的方法。根据结果​​，无论捕获和照明的几何形状如何，桑德森表面校正都能成功地补偿分光光度法和分光光度法测量值之间的差异。该模型还用于使用哑光，半光泽和光泽彩色目标作为训练数据集来表征数码相机。与传统方法相比，基于Saunderson的模型改进了变换矩阵的几何形状。而且，结果证实了基于线性矩阵运算的Saunderson表面校正的更简单推导的有效性。

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  • 来源
    《Journal of Imaging Science and Technology》 |2013年第5期|050503.1-050503.15|共15页
  • 作者

    Farhad Moghareh Abed; Roy S. Berns; Kenichiro Masaoka;

  • 作者单位

    Munsell Color Science Laboratory, Rochester Institute of Technology, Rochester, 14623, NY;

    Munsell Color Science Laboratory, Rochester Institute of Technology, Rochester, 14623, NY;

    NHK Science & Technology Research Laboratories 1-10-11 Kinuta, Setagaya, Tokyo 157-8510, Japan;

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