Abstract. Color constancy algorithms can provide us with illuminant invariant descriptions for a scene, and it is often accomplished by illuminant estimation. Most statistics-based methods estimate the illuminant color from the information provided by all pixels of an image. However, this research reveals that, for most images, the color of many pixels is quite different from the illuminant, and these pixels may severely trim the performance of statistics-based methods. Based on this fact, we propose a color constancy algorithm that finds a subset of image pixels with r, g components similar to those of the illuminant through a shallow neural network, and this subset of pixels is called illuminant close pixels (ICPs). Then the illuminant color is estimated from these pixels by some statistics-based methods. The proposed method has been evaluated and investigated on two benchmark datasets. Compared to using all pixels in an image, these statistics-based methods have been efficiently improved using ICPs.

中文翻译：

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使用在 rg 色度空间中空间上靠近光源的像素进行光源估计

摘要。颜色恒常性算法可以为我们提供场景的光源不变描述，并且通常通过光源估计来完成。大多数基于统计的方法从图像的所有像素提供的信息中估计光源颜色。然而，这项研究表明，对于大多数图像，许多像素的颜色与光源有很大不同，这些像素可能会严重影响基于统计方法的性能。基于这一事实，我们提出了一种颜色恒常性算法，通过浅层神经网络找到具有与光源相似的r、g分量的图像像素子集，该像素子集称为光源近像素（ICPs）。然后通过一些基于统计的方法从这些像素估计光源颜色。所提出的方法已经在两个基准数据集上进行了评估和研究。与使用图像中的所有像素相比，这些基于统计的方法使用 ICP 得到了有效改进。