Abstract
For the purpose of promoting the research of deep neural networks, image stylization has received extensive attention. Researchers have proposed a huge number of image stylization methods based on deep neural networks. In this paper, image stylization methods are divided into two categories: image stylization method based on reference and based on domain. A series of stylization methods. Several classicals methods are summarized, and the performance of the two categories of stylization methods on the common data sets of image stylization tasks are compared respectively, the developments of image stylization in video, human face and brushwork are proposed, the current progress of image stylization and the future research direction are mentioned. At the end of the article, the full text is summarized and summarized.
Publisher
Darcy & Roy Press Co. Ltd.
Reference21 articles.
1. Gatys L A,Eckras, Betchem. Image Style Transfer Using Convolutional Neural Networks, IEEE Conference on Computer Vision and Pattern Recognition, 2016:2414-2423
2. Johnson J, Alah I am, Li F F. Perceptual Losses for Real. Time Style Transfer and Super.Resolution. European Con.ference on Computer Vision, 2016:694-711.
3. Tu Pengqi, Gao Changxin, Sang Nong: A Review of Image Stylization Methods Based on Deep Neural Networks, 2022, 35(4):333-347.
4. Li C, Wang M. Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis, IEEE Conference on Computer Vision, and Pattern Recognition, 2016: 2479-2486.
5. Chen T Q, Schmidt M. Fast Patch⁃Based Style Transfer of Arbitrary Style. https: // arxiv.org / pdf / 1612. 04337.pdf.