Name
Title | ||
|---|---|---|
Contour-enhanced CycleGAN framework for style transfer from scenery photos to Chinese landscape paintings |
Abstract | ||
|---|---|---|
Image style transfer based on the generative adversarial network model has become an important research field. Among these generative adversarial network models, a distinct advantage of CycleGAN is that it can transfer between multiple domains when the data is not paired. To approximate the effects of the texturing method with the characteristics of traditional Chinese painting—"Cun method", this paper proposes an image style transfer framework to realize the transfer from scenery photos to Chinese landscape paintings. We design a contour-enhancing translation branch, which effectively guides the transfer from photos to paintings with edge detection operators computing the gradient maps. Simulation results show that this method can convert real scenery photos to Chinese landscape paintings. The Inception Score shows that contour enhancement can make the generated set performs better on sensitivity to image edges. The Kernel Inception distance and Inception-based Structural Similarity between the generated image and the "Cun method" data set shows that contour enhancement can make the generated image closer to the "Cun method" effect. Compared with Kernel Inception distance and Frechet-Inception Distance, the Inception-based Structural Similarity proposed in this paper directly focuses on similarity, the similarities between the mean features of images generated by our model, and the "Cun method" set is 97.89%, and the composite similarity metric being 0.92. The method also performs better than the MUNIT, NiceGAN, CycleGAN, and U-GAT-IT reference models under the Neural Image Assessment metric. This indicates that the introduction of the edge operator makes the generated landscape paintings more aesthetic, especially in situations where scenery photos are rich in edge information. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s00521-022-07432-w | Neural Computing and Applications |
Keywords | DocType | Volume |
GAN, Artificial intelligence art, Chinese painting, Edge-enhanced, Style transfer, Cun method, ISSIM | Journal | 34 |
Issue | ISSN | Citations |
20 | 0941-0643 | 0 |
PageRank | References | Authors |
0.34 | 8 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Xianlin | 1 | 0 | 0.34 |
| Peng Shenglin | 2 | 0 | 0.34 |
| Hu Qiyao | 3 | 0 | 0.34 |
| Jinye Peng | 4 | 284 | 40.93 |
| Wang Jiaxin | 5 | 0 | 0.34 |
| Liu Xinyu | 6 | 0 | 0.34 |
| Jianping Fan | 7 | 2677 | 192.33 |