Skip to main content
SpringerLink
Log in

Improving Picture Quality with Photo-Realistic Style Transfer

  • Conference paper
  • First Online:
Image Analysis and Recognition (ICIAR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10882))

Included in the following conference series:

Abstract

In this paper, we study style transfer applications for the photo-realistic image processing tasks. First, we present the results on image quality improvement based with photo style transfer. Second, we describe the problems of learning style transfer under geometrical constraints for processing portrait images and multi-style transfer. Finally, we give a short glimpse on application of image-to-image translation methods for updating realistic graphics for video games.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gatys, L.A., Alexander, S., Ecker, M.B.: A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576 (2015)

  2. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)

    Google Scholar 

  3. Gatys, L.A., Ecker, A.S., Bethge, M.: Texture synthesis using convolutional neural networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems 28: Annual Conference on NIPS 2015, 7–12 December 2015, Montreal, Quebec, Canada, pp. 262–270 (2015)

    Google Scholar 

  4. Ulyanov, D., Lebedev, V., Vedaldi, A., Lempitsky, V.S.: Texture networks: feed-forward synthesis of textures and stylized images. In: Balcan, M., Weinberger, K.Q. (eds) Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, 19–24 June 2016, New York City, NY, USA, JMLR Workshop and Conference Proceedings, JMLR.org, vol. 48, pp. 1349–1357 (2016)

    Google Scholar 

  5. Chen, D., Yuan, L., Liao, J., Yu, N., Hua, G.: Stylebank: An explicit representation for neural image style transfer. CoRR abs/1703.09210 (2017)

    Google Scholar 

  6. Wilmot, P., Risser, E., Barnes, C.: Stable and controllable neural texture synthesis and style transfer using histogram losses. CoRR abs/1701.08893 (2017)

    Google Scholar 

  7. Gatys, L.A., Bethge, M., Hertzmann, A., Shechtman, E.: Preserving color in neural artistic style transfer. CoRR abs/1606.05897 (2016)

    Google Scholar 

  8. Jetchev, N., Bergmann, U., Vollgraf, R.: Texture synthesis with spatial generative adversarial networks. CoRR abs/1611.08207 (2016)

    Google Scholar 

  9. Wang, X., Gupta, A.: Generative image modeling using style and structure adversarial networks. CoRR abs/1603.05631 (2016)

    Chapter  Google Scholar 

  10. Li, C., Wand, M.: Combining markov random fields and convolutional neural networks for image synthesis. CoRR abs/1601.04589 (2016)

    Google Scholar 

  11. Kwatra, V., Essa, I.A., Bobick, A.F., Kwatra, N.: Texture optimization for example-based synthesis. ACM Trans. Graph. 24(3), 795–802 (2005)

    Article  Google Scholar 

  12. Elad, M., Milanfar, P.: Style-transfer via texture-synthesis. CoRR abs/1609.03057 (2016)

    Google Scholar 

  13. Gatys, L.A., Ecker, A.S., Bethge, M., Hertzmann, A., Shechtman, E.: Controlling perceptual factors in neural style transfer. CoRR abs/1611.07865 (2016)

    Google Scholar 

  14. Zhao, H., Jin, X., Shen, J., Wei, F.: Real-time photo style transfer. In: 11th International Conference on Computer-Aided Design and Computer Graphics, CAD/Graphics 2009, 19–21 August 2009, Huangshan, China, pp. 140–145. IEEE (2009)

    Google Scholar 

  15. Wang, X., Oxholm, G., Zhang, D., Wang, Y.: Multimodal transfer: A hierarchical deep convolutional neural network for fast artistic style transfer. CoRR abs/1612.01895 (2016)

    Google Scholar 

  16. Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, 27–30 June 2016, Las Vegas, NV, USA, pp. 2414–2423. IEEE Computer Society (2016)

    Google Scholar 

  17. Luan, F., Paris, S., Shechtman, E., Bala, K.: Deep photo style transfer. arXiv preprint arXiv:1703.07511 (2017)

  18. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, Part II, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43

    Chapter  Google Scholar 

  19. Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss functions for neural networks for image processing. CoRR abs/1511.08861 (2015)

    Google Scholar 

  20. Liu, J., Yang, W., Sun, X., Zeng, W.: Photo stylistic brush: Robust style transfer via superpixel-based bipartite graph. CoRR abs/1606.03871 (2016)

    Google Scholar 

  21. Kartashov, E.: Lesson # 19 shooting portraits on a black background (2017). https://photoshop-master.org/disc92/

  22. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004 (2016)

  23. Rockstar North, W.D.S.: Grand theft auto: San andreas (2004). http://www.rockstargames.com/sanandreas/. Accessed 26 Oct 2004

  24. Rockstar Games, R.N.: Grand theft auto: V (2013). http://www.rockstargames.com/V/. Accessed 17 Sept 2013

  25. Liu, M.Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in Neural Information Processing Systems, NIPS, pp. 469–477 (2016)

    Google Scholar 

  26. Kim, T., Cha, M., Kim, H., Lee, J., Kim, J.: Learning to discover cross-domain relations with generative adversarial networks. arXiv preprint arXiv:1703.05192 (2017)

  27. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593 (2017)

  28. Ulyanov, D., Vedaldi, A., Lempitsky, V.S.: Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis. CoRR abs/1701.02096 (2017)

    Google Scholar 

  29. Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: International Conference on Computer Vision (ICCV), vol. 1. IEEE (2017)

    Google Scholar 

  30. Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional gans. arXiv preprint arXiv:1711.11585 (2017)

Download references

Acknowledgements

The article was supported within the framework of a subsidy by the Russian Academic Excellence Project ‘5–100’.

Author information

Authors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Ilya Makarov, Diana Polonskaya & Anastasia Feygina

Authors
  1. Ilya Makarov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diana Polonskaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anastasia Feygina
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ilya Makarov .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  3. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Bart ter Haar Romeny

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Makarov, I., Polonskaya, D., Feygina, A. (2018). Improving Picture Quality with Photo-Realistic Style Transfer. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93000-8_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92999-6

  • Online ISBN: 978-3-319-93000-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation