Neural Artistic Style and Color Transfer Using Deep Learning

TL;DR

This paper presents a novel deep learning-based method that combines neural artistic style transfer with color transfer, quantitatively evaluating various algorithms using KL divergence and histogram matching techniques.

Contribution

It introduces a methodology integrating neural style transfer with color transfer and employs KL divergence for quantitative evaluation of algorithms.

Findings

Reinhard global color transfer performs well in style transfer.

IDT with regrain improves color histogram matching.

The proposed method effectively combines artistic style with color correction.

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.