Image Style Transfer: from Artistic to Photorealistic
It provides a comprehensive overview for researchers and practitioners in computer vision and multimedia, but is incremental as it synthesizes existing work without introducing new methods.
This review paper surveys the development of photorealistic style transfer, tracing its evolution from artistic style transfer and highlighting contributions from traditional image processing techniques, with a focus on VGG-based methods, whitening and coloring transforms, and hybrid deep learning approaches.
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.