# Neural Artistic Style and Color Transfer Using Deep Learning

arXiv: https://arxiv.org/abs/2508.08608 · HTML: https://mlsanity.com/paper/2508.08608/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Visual Attribute Transfer through Deep Image Analogy](https://mlsanity.com/paper/1705.01088/) (2017)
- [Preserving Color in Neural Artistic Style Transfer](https://mlsanity.com/paper/1606.05897/) (2016)
- [Unsupervised Total Variation Loss for Semi-supervised Deep Learning of Semantic Segmentation](https://mlsanity.com/paper/1605.01368/) (2016)
- [Incorporating long-range consistency in CNN-based texture generation](https://mlsanity.com/paper/1606.01286/) (2016)
- [Image color transfer to evoke different emotions based on color combinations](https://mlsanity.com/paper/1307.3581/) (2013)
- [An Overview of Color Transfer and Style Transfer for Images and Videos](https://mlsanity.com/paper/2204.13339/) (2022)

## Abstract (arXiv)

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.