I explore the mathematical transformation that occurs in color coordinate
space when physically mixing paints of two different colors. I tested 120 pairs
of 16 paint colors and used a linear regression to find the most accurate
combination of input parameters, both in RGB space and several other color
spaces. I found that the fit with the strongest coefficient of determination
was a geometrically symmetrized linear combination of the colors in CIEXYZ
space, while this same mapping in RGB space returns a better mean squared
error.

在混合两种不同颜色的颜料时，我研究了在颜色坐标空间中发生的数学变换。通过测试 16 种颜色的 120 对颜料，我使用线性回归找到了在 RGB 空间和其他几个颜色空间中的最准确的输入参数组合。我发现，在 CIEXYZ 空间中，具有最强确定系数的拟合是颜色的几何对称化线性组合，而在 RGB 空间中，使用同样的映射会得到更好的均方误差。

混合颜料：使用多元线性回归分析多个坐标空间中的色彩值转换

Mixing Paint: An analysis of color value transformations in multiple  coordinate spaces using multivariate linear regression

Noise removal in the standard RGB (sRGB) space remains a challenging task, in
that the noise statistics of real-world images can be different in R, G and B
channels. In fact, the green channel usually has twice the sampling rate in raw
data and a higher signal-to-noise ratio than red/blue ones. However, the green
channel prior (GCP) is often understated or ignored in color image denoising
since many existing approaches mainly focus on modeling the relationship among
image patches. In this paper, we propose a simple and effective one step
GCP-based image denoising (GCP-ID) method, which aims to exploit the GCP for
denoising in the sRGB space by integrating it into the classic nonlocal
transform domain denoising framework. Briefly, we first take advantage of the
green channel to guide the search of similar patches, which improves the patch
search quality and encourages sparsity in the transform domain. Then we
reformulate RGB patches into RGGB arrays to explicitly characterize the density
of green samples. The block circulant representation is utilized to capture the
cross-channel correlation and the channel redundancy. Experiments on both
synthetic and real-world datasets demonstrate the competitive performance of
the proposed GCP-ID method for the color image and video denoising tasks. The
code is available at github.com/ZhaomingKong/GCP-ID.

RGB 空间中的噪声去除仍然是一个具有挑战性的任务，因为现实世界图像的噪声统计数据在红、绿和蓝通道上可能存在差异。本文提出了一种简单有效的基于绿色通道优先（GCP）的图像去噪方法，目的是在 sRGB 空间中利用 GCP 将其集成到经典的非本地变换域去噪框架中。实验结果表明，该方法在彩色图像和视频去噪任务中具有竞争力的性能。

基于绿色通道先验的彩色图像去噪

Color Image Denoising Using The Green Channel Prior

Grounding is crucial for natural language understanding. An important subtask
is to understand modified color expressions, such as 'dirty blue'. We present a
model of color modifiers that, compared with previous additive models in RGB
space, learns more complex transformations. In addition, we present a model
that operates in the HSV color space. We show that certain adjectives are
better modeled in that space. To account for all modifiers, we train a hard
ensemble model that selects a color space depending on the modifier color pair.
Experimental results show significant and consistent improvements compared to
the state-of-the-art baseline model.

本研究展示了一个对于自然语言中颜色修饰的理解模型，该模型比之前在 RGB 颜色空间中的加法模型能够学习更复杂的变换，同时我们还提出了一个在 HSV 颜色空间中操作的模型，结果表明针对某些形容词在 HSV 颜色空间中效果更好，此外我们还训练了一种硬强集成模型，该模型会根据修饰符颜色配对情况来选择颜色空间，实验结果表明与现有基线模型相比，该模型有显著且一致的提升。