The Visual Computer

, Volume 30, Issue 9, pp 969–979 | Cite as

Artistic preprocessing for painterly rendering and image stylization

Original Article
First Online:

Abstract

A practical image enhancing technique is presented as a preprocessing step for painterly rendering and image stylization, which transforms the input image mimicking the vision of artists. The new method contains mainly two parts dealing with artistic enhancement and color adjustment, respectively. First, through feature extraction and simplification, an abstract shadow map is constructed for the input image, which is then taken as a guide for emphasizing the light–shadow contrast and the important shadow lines. Next, to simulate the intense color emotion often subjectively added by the artist, a color adjustment technique is proposed to generate lively colors with sharp contrast imitating the artistic vision. The preprocessing operation is compatible with existing stylization and stroke-based painterly rendering techniques, and it can also produce different types of stylization results independently.

Keywords

Image processing Example based painting Color features learning 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work is supported by Tsinghua—Tencent Joint Laboratory for Internet Innovation Technology under Grant No. 2012-01 and the National Natural Science Foundation of China (No. 60970068). The authors would also like to thank the support from the International Joint Project from the Royal Society of UK (No. JP100987.)

References

  1. 1.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001) CrossRefGoogle Scholar
  2. 2.
    Cheng, M., Zhang, G., Mitra, N., Huang, X., Hu, S.: Global contrast based salient region detection. In: Proceedings of Computer Vision and Pattern Recognition. IEEE Press, New York (2011) Google Scholar
  3. 3.
    Collomosse, J., Rowntree, D., Hall, P.: Stroke surfaces: temporally coherent artistic animations from video. IEEE Trans. Vis. Comput. Graph. 11(4), 540–549 (2005) CrossRefGoogle Scholar
  4. 4.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002) CrossRefGoogle Scholar
  5. 5.
    Cong, L., Tong, R., Dong, J.: Selective image abstraction. Vis. Comput. 27(3), 187–198 (2011) CrossRefGoogle Scholar
  6. 6.
    DeCarlo, D., Santella, A.: Stylization and abstraction of photographs. ACM Trans. Graph. 21(3), 769–776 (2002) Google Scholar
  7. 7.
    Gooch, B., Reinhard, E., Gooch, A.: Human facial illustrations: creation and psychophysical evaluation. ACM Trans. Graph. 23(1), 27–44 (2004) CrossRefGoogle Scholar
  8. 8.
    Hays, J., Essa, I.: Image and video based painterly animation. In: Proceedings of the International Symposium on Non-Photorealistic Animation and Rendering, pp. 113–120. ACM, New York (2004) CrossRefGoogle Scholar
  9. 9.
    Hertzmann, A.: Painterly rendering with curved brush strokes of multiple sizes. In: Proceedings of SIGGRAPH, pp. 453–460. ACM, New York (1998) Google Scholar
  10. 10.
    Hertzmann, A.: Non-photorealistic rendering and the science of art. In: Proceedings of the 8th International Symposium on Non-Photorealistic Animation and Rendering, pp. 147–157 (2010) Google Scholar
  11. 11.
    Hu, S.-M., Chen, T., Xu, K., Cheng, M.-M., Martin, R.R.: Internet visual media processing: a survey with graphics and vision applications. Vis. Comput. 29(5), 393–405 (2013) CrossRefGoogle Scholar
  12. 12.
    Huang, Y., Ralph, M., Paul, L., Meng, X., Yang, C.: Expressive line drawings of human faces from range images. Sci. China, Ser. F, Inf. Sci. 52(2), 295–307 (2009) CrossRefMATHGoogle Scholar
  13. 13.
    Huang, H., Zhang, L., Fu, T.: Video painting via motion layer manipulation. Comput. Graph. Forum 29(7), 2055–2064 (2010) CrossRefGoogle Scholar
  14. 14.
    Huang, H., Fu, T., Li, C.: Painterly rendering with content-dependent natural paint strokes. Vis. Comput. 27(9), 1–11 (2011) CrossRefGoogle Scholar
  15. 15.
    Hulusic, V., Debattista, K., Chalmers, A.: Smoothness perception. Vis. Comput. 29(11), 1159–1172 (2013) CrossRefGoogle Scholar
  16. 16.
    Kagaya, M., Brendel, W., Deng, Q., Kesterson, T., Todorovic, S., Neill, P., Zhang, E.: Video painting with space-time-varying style parameters. IEEE Trans. Vis. Comput. Graph. 17, 74–87 (2010) CrossRefGoogle Scholar
  17. 17.
    Kang, H., Lee, S., Chui, C.: Coherent line drawing. In: Proceedings of the International Symposium on Non-Photorealistic Animation and Rendering, pp. 43–50. ACM, New York (2007) Google Scholar
  18. 18.
    Kang, H., Lee, S., Chui, C.: Flow-based image abstraction. IEEE Trans. Vis. Comput. Graph. 15(1), 62–76 (2009) CrossRefGoogle Scholar
  19. 19.
    Kyprianidis, J., Kang, H.: Image and video abstraction by coherence-enhancing filtering. Comput. Graph. Forum 30(2), 593–602 (2011) CrossRefGoogle Scholar
  20. 20.
    Kyprianidis, J.E., Collomosse, J., Wang, T., Isenberg, T.: State of the art: a taxonomy of artistic stylization techniques for images and video. IEEE Trans. Vis. Comput. Graph. 19(5), 866–885 (2013) CrossRefGoogle Scholar
  21. 21.
    Lee, H., Lee, C.H., Yoon, K.: Motion based painterly rendering. Comput. Graph. Forum 28(4), 1207–1215 (2009) CrossRefGoogle Scholar
  22. 22.
    Li, P., Sun, H.Q., Sheng, B., Shen, J.B.: Image stylization with enhanced structure on GPU. Sci. China, Inf. Sci. 55(5), 1093–1105 (2012) CrossRefMathSciNetGoogle Scholar
  23. 23.
    Liao, P., Chen, T., Chung, P.: A fast algorithm for multilevel thresholding. J. Inf. Sci. Eng. 17(5), 713–728 (2001) Google Scholar
  24. 24.
    Litwinowicz, P.: Processing images and video for an impressionist effect. In: Proceedings of SIGGRAPH, pp. 407–414 (1997) CrossRefGoogle Scholar
  25. 25.
    Liu, L., Chen, R., Wolf, L., Cohen-Or, D.: Optimizing photo composition. Comput. Graph. Forum 29(2), 469–478 (2010) CrossRefGoogle Scholar
  26. 26.
    Liu, Y., Luo, X., Huan, Y., Chen, W., Fu, X.: Image retargeting quality assessment. Comput. Graph. Forum 30(2), 583–592 (2011) CrossRefGoogle Scholar
  27. 27.
    Mould, D., Grant, K.: Stylized black and white images from photographs. In: Proceedings of the International Symposium on Non-Photorealistic Animation and Rendering, pp. 49–58. ACM, New York (2008) Google Scholar
  28. 28.
    O’Donovan, P., Hertzmann, A.: Anipaint: interactive painterly animation from video. IEEE Trans. Vis. Comput. Graph. 18(3), 475–487 (2012) CrossRefGoogle Scholar
  29. 29.
    Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11(3), 285–296 (1975) CrossRefGoogle Scholar
  30. 30.
    Raskar, R., Tan, K., Feris, R., Yu, J., Turk, M.: Non-photorealistic camera: depth edge detection and stylized rendering using multi-flash imaging. ACM Trans. Graph. 23(3), 679–688 (2004) CrossRefGoogle Scholar
  31. 31.
    Sun, Y., Fisher, R.: Object-based visual attention for computer vision. Artif. Intell. 146(1), 77–123 (2003) CrossRefMATHMathSciNetGoogle Scholar
  32. 32.
    Torralba, A., Oliva, A., Castelhano, M., Henderson, J.: Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. Psychol. Rev. 113(4), 766 (2006) CrossRefGoogle Scholar
  33. 33.
    Tsai, W.: Moment-preserving thresholding: a new approach. Comput. Vis. Graph. Image Process. 29(3), 377–393 (1985) CrossRefGoogle Scholar
  34. 34.
    Turner, M.R.: Texture discrimination by Gabor functions. Biol. Cybern. 55(2), 71–82 (1986) Google Scholar
  35. 35.
    Wang, J., Xu, Y., Shum, H., Cohen, M.: Video tooning. In: Proceedings of ACM SIGGRAPH, pp. 574–583. ACM, New York (2004) Google Scholar
  36. 36.
    Wang, S., Cai, K., Lu, J., Liu, X., Wu, E.: Real-time coherent stylization for augmented reality. Vis. Comput. 26(6), 445–455 (2010) CrossRefGoogle Scholar
  37. 37.
    Wen, F., Luan, Q., Liang, L., Xu, Y., Shum, H.: Color sketch generation. In: Proceedings of the International Symposium on Non-Photorealistic Animation and Rendering, pp. 47–54. ACM, New York (2006) CrossRefGoogle Scholar
  38. 38.
    Winnemöller, H., Olsen, S.C., Gooch, B.: Real-time video abstraction. ACM Trans. Graph. 25(3), 1221–1226 (2006) CrossRefGoogle Scholar
  39. 39.
    Wong, F.J., Takahashi, S.: Abstracting images into continuous-line artistic styles. Vis. Comput. 29(6–8), 729–738 (2013) CrossRefGoogle Scholar
  40. 40.
    Xiao, X., Ma, L.: Gradient-preserving color transfer. Comput. Graph. Forum 28(7), 1879–1886 (2009) CrossRefGoogle Scholar
  41. 41.
    Xu, J., Kaplan, C.: Artistic thresholding. In: Proceedings of the International Symposium on Non-Photorealistic Animation and Rendering, pp. 39–47. ACM, New York (2008) Google Scholar
  42. 42.
    Zeng, K., Zhao, M., Xiong, C., Zhu, S.: From image parsing to painterly rendering. ACM Trans. Graph. 29(1), 2 (2009) CrossRefGoogle Scholar
  43. 43.
    Zhang, S., Chen, T., Zhang, Y., Hu, S., Ralph, M.: Video-based running water animation in Chinese painting style. Sci. China, Ser. F, Inf. Sci. 52(2), 162–171 (2009) CrossRefGoogle Scholar
  44. 44.
    Zhang, S.H., Li, X.Y., Hu, S.M., Martin, R.R.: Online video stream abstraction and stylization. IEEE Trans. Multimed. 13(6), 1286–1294 (2011) CrossRefGoogle Scholar
  45. 45.
    Zhao, M., Zhu, S.: Sisley the abstract painter. In: Proceedings of the 8th International Symposium on Non-Photorealistic Animation and Rendering, pp. 99–107. ACM, New York (2010) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Electrical Information EngineeringXi’an Jiaotong UniversityShaanxiChina
  2. 2.Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina
  3. 3.College of EngineeringSwansea UniversityWalesUK

Personalised recommendations