Abstract
Neural artistic style transfers and blends the content and style representation of one image with the style of another. This enables artists to create unique innovative visuals and enhances artistic expression in various fields including art, design, and film. Color transfer algorithms are an important in digital image processing by adjusting the color information in a target image based on the colors in the source image. Color transfer enhances images and videos in film and photography, and can aid in image correction. We introduce a methodology that combines neural artistic style with color transfer. The method uses the Kullback-Leibler (KL) divergence to quantitatively evaluate color and luminance histogram matching algorithms including Reinhard global color transfer, iteration distribution transfer (IDT), IDT with regrain, Cholesky, and PCA between the original and neural artistic style transferred image using deep learning. We estimate the color channel kernel densities. Various experiments are performed to evaluate the KL of these algorithms and their color histograms for style to content transfer.
Abstract (translated)
神经艺术风格转移将一幅图像的内容和风格表示与另一幅图像的风格相结合，这使艺术家能够创造出独特的创新视觉效果，并在包括艺术、设计和电影在内的各个领域增强艺术表达。颜色传输算法是数字图像处理中的重要组成部分，通过基于源图像的颜色来调整目标图像中的颜色信息。颜色传输可以提升影视作品的质量，在摄影和视频制作中起到关键作用，并有助于图像校正。我们介绍了一种结合神经艺术风格转移与色彩转换的方法论。该方法使用Kullback-Leibler (KL) 散度，利用深度学习技术对原始图像和经神经艺术风格处理后的图像之间的颜色和亮度直方图匹配算法进行定量评估，包括Reinhard全局颜色传输、迭代分布传输(IDT)、IDT结合重粒化（regrain）、Cholesky分解以及主成分分析(PCA)。我们估计了色彩通道的核密度。通过各种实验来评估这些算法及其在风格到内容转换过程中的KL散度和颜色直方图。 这种方法论提供了一种量化和比较不同色彩传输技术与神经艺术风格转移结合效果的新途径，从而有助于改进图像处理技术和增强视觉创作能力。
URL
https://arxiv.org/abs/2508.08608
