Image Style Transfer: from Artistic to Photorealistic
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 48 references that are in the map. First posted on arXiv 12 Mar 2022.
Builds on
13 of 48 references.
References · 13
- 2022Generative Adversarial Networkscited by 5,627
- 2017Generative Adversarial Networks: An Overviewcited by 590
- 2017Universal Style Transfer via Feature Transformscited by 354
- 2022Splicing ViT Features for Semantic Appearance Transfercited by 209
- 2017Deep Photo Style Transfercited by 168
- 2019Photorealistic Style Transfer via Wavelet Transformscited by 166
- 2018A Closed-form Solution to Photorealistic Image Stylizationcited by 133
- 20213D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Imagecited by 45
- 2021PhotoWCT2: Compact Autoencoder for Photorealistic Style Transfer Resulting from Blockwise Training and Skip Connections of High-Frequency Residualscited by 30
- 2020Real-time Localized Photorealistic Video Style Transfercited by 24
- 2017Photorealistic Style Transfer with Screened Poisson Equationcited by 9
- 2018Segmentation of Arterial Walls in Intravascular Ultrasound Cross-Sectional Images Using Extremal Region Selectioncited by 4
Built on by
Not cited yet.