- PII
- 10.31857/S0132347424030029-1
- DOI
- 10.31857/S0132347424030029
- Publication type
- Article
- Status
- Published
- Authors
- Volume/ Edition
- Volume / Issue number 3
- Pages
- 14-20
- Abstract
- Image style transfer is an applied task for automatic rendering of the original image (content) in the style of another image (specifying the target style). Traditional image stylization methods provide only a single stylization result. If the user is not satisfied with it due to stylization artifacts, he has to choose a different style. The work proposes a modified stylization algorithm, giving a variety of stylization results, and achieves improved stylization quality by using additional style information from similar styles.
- Keywords
- генерация изображений обработка изображений нейронные сети
- Date of publication
- 15.06.2024
- Year of publication
- 2024
- Number of purchasers
- 0
- Views
- 46
References
- 1. Gooch B., Gooch A. Non-photorealistic rendering. CRC Press, 2001.
- 2. Strothotte T., Schlechtweg S. Non-photorealistic computer graphics: modeling, rendering, and animation. Morgan Kaufmann, 2002.
- 3. Rosin P., Collomosse J. Image and video-based artistic stylisation. Springer Science & Business Media, 2012. V. 42.
- 4. Gatys L.A., Ecker A.S., Bethge M. A neural algorithm of artistic style // arXiv preprint arXiv:1508.06576, 2015.
- 5. Huang X., Belongie S. Arbitrary style transfer in real-time with adaptive instance normalization // Proceedings of the IEEE international conference on computer vision. 2017. P. 1501–1510.
- 6. Li Y. et al. Universal style transfer via feature transforms // Advances in neural information processing systems. 2017. V. 30.
- 7. Simonyan K., Zisserman A. Very deep convolutional networks for large-scale image recognition // arXiv preprint arXiv:1409.1556, 2014.
- 8. Deng J. et al. Imagenet: A large-scale hierarchical image database // 2009 IEEE conference on computer vision and pattern recognition. IEEE. 2009. P. 248–255.
- 9. Phillips F., Mackintosh B. Wiki Art Gallery, Inc.: A case for critical thinking // Issues in Accounting Education. 2011. V. 26. № 3. P. 593–608.
- 10. Florea C. et al. Pandora: Description of a painting database for art movement recognition with baselines and perspectives // 2016 24th European Signal Processing Conference (EUSIPCO). IEEE. 2016. P. 918–922.
- 11. Buzuloiu V. et al. Adaptive-neighborhood histogram equalization of color images // Journal of Electronic Imaging. 2001. V. 10. № 2. P. 445–459.
- 12. de Villiers J.P. A comparison of image sharpness metrics and real-time sharpening methods with GPU implementations // Proceedings of the 7th International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa. 2010. P. 53–62.
- 13. Hassen R., Wang Z., Salama M.M.A. Image sharpness assessment based on local phase coherence // IEEE Transactions on Image Processing. 2013. V. 22. № 7. P. 2798–2810.
- 14. Sanakoyeu A. et al. A style-aware content loss for real-time hd style transfer // Proceedings of the European conference on computer vision (ECCV). 2018. P. 698–714.
- 15. Huang X., Belongie S. Arbitrary style transfer in real-time with adaptive instance normalization // Proceedings of the IEEE international conference on computer vision. 2017. P. 1501–1510.
- 16. Li Y. et al. Universal style transfer via feature transforms // Advances in neural information processing systems. 2017. V. 30.
