用深度学习融合风格迁移与色彩迁移，生成艺术感更强的图像。
Neural Artistic Style and Color Transfer Using Deep Learning
- 结合深度学习的风格迁移与色彩迁移方法
- 通过KL散度评估多种色彩匹配算法效果
- 适合数字艺术、影视后期等创意领域
神经艺术风格迁移将一张图像的内容与另一张图像的风格相结合，使艺术家能创作出独特的视觉作品，并在艺术、设计和电影等领域增强表现力。色彩迁移是数字图像处理中的关键技术，通过调整目标图像的颜色信息以匹配源图像的色彩特征，广泛应用于影视与摄影的图像增强及修复。本文提出一种融合神经艺术风格与色彩迁移的方法，利用Kullback-Leibler（KL）散度量化评估包括Reinhard全局色彩迁移、迭代分布转移（IDT）、带重颗粒的IDT、Cholesky和PCA在内的多种色彩与亮度直方图匹配算法，在深度学习框架下对原始图像与风格迁移后图像进行对比。通过估计颜色通道的核密度，系统性地测试了这些算法在风格到内容迁移中的表现。
原文摘要 · Abstract (English)
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.
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