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Reflectance and illuminant estimation for digital cameras.

机译:数码相机的反射率和光源估计。

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摘要

Several important problems in color imaging can be traced to differences in how cameras and humans sample the spectral properties of light. Color processing within the imaging pipeline, loosely referred to as color correction, transforms the sampled camera responses to a form that matches the human responses. The accuracy of the color correction transformation is limited for two reasons. First, the human visual system and most color acquisition devices critically undersample the spectral information, making the differences in their sampling functions quite significant. Second, the human visual system derives a relatively constant surface color appearance despite variations in the illuminant, complicating color correction with the need to estimate the illuminant.; Assuming complete knowledge of the illuminant, we formulate color correction as an input-referred estimation problem. In particular, we analyze how a small number of camera measurements can be used to estimate a complete spectral surface reflectance function. We introduce conventional linear color transformations, and then extend these transformations using forms of local linear regression that we refer to as submanifold estimation methods. These methods are based on the observation that for many data sets the deviations between the signal and the linear estimate is systematic; submanifold methods incorporate knowledge of these systematic deviations to improve upon linear estimation methods. We describe the geometric intuition of these methods and evaluate the submanifold method on printed material data and hyperspectral image data.; Next, we discard the assumption of complete knowledge of the illuminant and analyze a technique to estimate the illuminant. Conventional algorithms rely on statistical assumptions about the scene properties (surface reflectance functions and geometry) to estimate the ambient illuminant. We introduce a new illuminant estimation paradigm that uses an active imaging method to measure scene properties, ending the need to make significant assumptions about these properties. The method actively emits light into the scene and estimates the surface reflectances from a conventional image and an auxiliary image acquired with a flash; the ambient illuminant spectral composition is classified, in turn, using these estimated reflectance functions. We evaluate the method's stability with respect to changes in the scene statistics.
机译:彩色成像中的几个重要问题可以归因于照相机和人类如何采样光的光谱特性。成像管道内的颜色处理(通常称为颜色校正)将采样的相机响应转换为与人类响应相匹配的形式。由于两个原因,颜色校正变换的精度受到限制。首先,人类视觉系统和大多数颜色采集设备严重不足以对光谱信息进行采样,从而使其采样功能之间的差异非常明显。第二,尽管光源有变化,但人类视觉系统仍能获得相对恒定的表面颜色外观,使颜色校正复杂化,需要估计光源。假设对光源有完整的了解,我们将色彩校正公式化为输入参考的估计问题。特别是,我们分析了如何使用少量相机测量值来估算完整的光谱表面反射率函数。我们介绍了常规的线性颜色变换,然后使用称为子流形估计方法的局部线性回归形式扩展了这些变换。这些方法基于以下观察:对于许多数据集,信号和线性估计之间的偏差是系统的;子流形方法结合了这些系统偏差的知识,以改进线性估计方法。我们描述了这些方法的几何直觉,并评估了印刷材料数据和高光谱图像数据的子流形方法。接下来,我们放弃对光源完全了解的假设,并分析一种估算光源的技术。常规算法依赖于有关场景属性(表面反射率函数和几何形状)的统计假设来估计环境光源。我们引入了一种新的光源估计范例,该范例使用主动成像方法来测量场景属性,从而不再需要对这些属性做出重大假设。该方法主动向场景中发射光,并根据常规图像和闪光灯获取的辅助图像估算表面反射率。依次使用这些估计的反射率函数对环境光源光谱组成进行分类。我们根据场景统计数据的变化评估该方法的稳定性。

著录项

  • 作者

    DiCarlo, Jeffrey Michael.;

  • 作者单位

    Stanford University.;

  • 授予单位 Stanford University.;
  • 学科 Engineering Electronics and Electrical.; Physics Optics.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 86 p.
  • 总页数 86
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;光学;
  • 关键词

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