Image Style Transfer: from Artistic to Photorealistic

TL;DR

This paper reviews the evolution of photorealistic style transfer, emphasizing deep learning methods, traditional image processing contributions, and focusing on VGG-based and whitening/coloring transform techniques.

Contribution

It provides a comprehensive overview of photorealistic style transfer development, highlighting the integration of deep learning and traditional image processing methods.

Findings

VGG-based techniques are central to photorealistic style transfer.

Whitening and coloring transform (WCT) methods are effective.

Combining deep learning with traditional techniques enhances results.

Abstract

The rapid advancement of deep learning has significantly boomed the development of photorealistic style transfer. In this review, we reviewed the development of photorealistic style transfer starting from artistic style transfer and the contribution of traditional image processing techniques on photorealistic style transfer, including some work that had been completed in the Multimedia lab at the University of Alberta. Many techniques were discussed in this review. However, our focus is on VGG-based techniques, whitening and coloring transform (WCTs) based techniques, the combination of deep learning with traditional image processing techniques.