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Two Illuminant Estimation and User Correction Preference

机译:两个发光体估计和用户校正偏好

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This paper examines the problem of white-balance correction when a scene contains two illuminations. This is a two step process: 1) estimate the two illuminants; and 2) correct the image. Existing methods attempt to estimate a spatially varying illumination map, however, results are error prone and the resulting illumination maps are too low-resolution to be used for proper spatially varying white-balance correction. In addition, the spatially varying nature of these methods make them computationally intensive. We show that this problem can be effectively addressed by not attempting to obtain a spatially varying illumination map, but instead by performing illumination estimation on large sub-regions of the image. Our approach is able to detect when distinct illuminations are present in the image and accurately measure these illuminants. Since our proposed strategy is not suitable for spatially varying image correction, a user study is performed to see if there is a preference for how the image should be corrected when two illuminants are present, but only a global correction can be applied. The user study shows that when the illuminations are distinct, there is a preference for the outdoor illumination to be corrected resulting in warmer final result. We use these collective findings to demonstrate an effective two illuminant estimation scheme that produces corrected images that users prefer.
机译:本文介绍了当场景包含两个照明时的白平衡校正问题。这是两个步骤过程:1)估计两个发光体; 2)纠正图像。然而,现有方法尝试估计空间变化的照明图,然而,结果是易于误差,并且产生的照明图太低了,以用于适当的空间变化的白平衡校正。此外,这些方法的空间变化性质使得它们计算得密集。我们表明,通过不试图获得空间变化的照明图,可以有效地解决该问题,而是通过对图像的大子区域进行照明估计来有效地解决。我们的方法能够检测图像中存在不同的照明并准确测量这些光源。由于我们所提出的策略不适合于空间变化的图像校正,因此执行用户学习,以便在存在两个发光体时何时应该校正图像的偏好,但是仅可以应用全局校正。用户学习表明,当照明是不同的时,较偏好校正户外照明,从而导致更温暖的最终结果。我们使用这些集体调查结果来展示有效的两个光源估计方案,其产生用户更愿意的校正图像。

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