End-users, without knowledge in photography, desire to beautify their photos
to have a similar color style as a well-retouched reference. However, the
definition of style in recent image style transfer works is inappropriate. They
usually synthesize undesirable results due to transferring exact colors to the
wrong destination. It becomes even worse in sensitive cases such as portraits.
In this work, we concentrate on learning low-level image transformation,
especially color-shifting methods, rather than mixing contextual features, then
present a novel scheme to train color style transfer with ground-truth.
Furthermore, we propose a color style transfer named Deep Preset. It is
designed to 1) generalize the features representing the color transformation
from content with natural colors to retouched reference, then blend it into the
contextual features of content, 2) predict hyper-parameters (settings or
preset) of the applied low-level color transformation methods, 3) stylize
content to have a similar color style as reference. We script Lightroom, a
powerful tool in editing photos, to generate 600,000 training samples using
1,200 images from the Flick2K dataset and 500 user-generated presets with 69
settings. Experimental results show that our Deep Preset outperforms the
previous works in color style transfer quantitatively and qualitatively.

本文提出了一种名为 Deep Preset 的彩色风格转移方法，通过学习低级别图像颜色变换，设计出了一种通用功能，将内容中的自然颜色的特征概括成重新修饰的参考特征，并将其融合到内容的背景特征中，从而使其风格与参考相似。在 Lightroom 的 600,000 个训练样本测试中，该方法在颜色风格转换方面表现出色，既量化又定性优于以前的方法。