Skip to main content
SpringerLink
Log in

A novel image decomposition approach and its applications

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

The current state-of-the-art edge-preserving decomposition techniques may not be able to fully separate textures while preserving edges. This may generate artifacts in some applications, e.g., edge detection, texture transfer, etc. To solve this problem, a novel image decomposition approach based on explicit texture separation from large scale components of an image is presented. We first apply a Gaussian structure-texture decomposition, to separate the majority of textures out of the input image. However, residual textures are still visible around the strong edges. To remove these residuals, an asymmetric sampling operator is proposed and followed by a joint bilateral correction to remove an excessive blur effect. We demonstrate that our approach is well suited for the tasks such as texture transfer, edge detection, non-photorealistic rendering, and tone mapping. The results show our approach outperforms existing state-of-the-art image decomposition approaches.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Algorithm 1
Fig. 6
Algorithm 2
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph. 27(3), 67–76 (2008)

    Article  Google Scholar 

  2. Paris, S., Durand, F.: A fast approximation of the bilateral filter using a signal processing approach. Int. J. Comput. Vis. 81(1), 224–252 (2009)

    Article  Google Scholar 

  3. Fattal, R.: Edge-avoiding wavelets and their applications. ACM Trans. Graph. 28(3), 22 (2009)

    Article  Google Scholar 

  4. Bhat, P., Zitnick, C.L., Cohen, M., Curless, B.: Gradientshop: A gradient-domain optimization framework for image and video filtering. ACM Trans. Graph. 29(2), 1–14 (2010)

    Article  Google Scholar 

  5. Paris, S., Hasinoff, S.W., Kautz, J.: Local laplacian filters: Edge-aware image processing with a laplacian pyramid. In: ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH 2010) (2010)

    Google Scholar 

  6. Meyer, Y.: Oscillating Patterns in Image Processing and Nonlinear Evolution Equations. Am. Math. Soc., Providence (2001)

    MATH  Google Scholar 

  7. Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph. 21(3), 257–266 (2002)

    Article  Google Scholar 

  8. Eisemann, E., Durand, F.: Flash photography enhancement via intrinsic relighting. ACM Trans. Graph. 23(3), 673–678 (2004)

    Article  Google Scholar 

  9. Aujol, J.F., Gilboa, G., Chan, T., Osher, S.: Structure-texture image decomposition–modeling, algorithms and parameter selection. Int. J. Comput. Vis. 67(1), 111–136 (2006)

    Article  Google Scholar 

  10. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: The Sixth International Conference on Computer Vision, pp. 839–846 (1998)

    Google Scholar 

  11. Chen, J., Paris, S., Durand, F.: Real-time edge-aware image processing with the bilateral grid. ACM Trans. Graph. 26(3)

  12. Bae, S., Paris, S., Durand, F.: Two-scale tone management for photographic look. ACM Trans. Graph. 25, 637–645 (2006)

    Article  Google Scholar 

  13. Fattal, R., Agrawala, M., Rusinkiewicz, S.: Multiscale shape and detail enhancement from multi-light image collections. ACM Trans. Graph. 26

  14. Choudhury, P., Tumblin, J.: The trilateral filter for high contrast images and meshes. In: Proceedings of the 14th Eurographics Workshop on Rendering, pp. 186–196 (2003)

    Google Scholar 

  15. Baek, J., Jacobs, D.: Accelerating spatially varying gaussian filters. ACM Trans. Graph. 29(6), 169 (2010)

    Article  Google Scholar 

  16. Subr, K., Soler, C., Durand, F.: Edge-preserving multiscale image decomposition based on local extrema. ACM Trans. Graph. 28(5), 147 (2009)

    Article  Google Scholar 

  17. Farbman, Z., Fattal, R., Lischinski, D.: Diffusion maps for edge-aware image editing. ACM Trans. Graph. 29(6), 145 (2010)

    Article  Google Scholar 

  18. Bhat, P., Curless, B., Cohen, M., Zitnick, C.: Fourier analysis of the 2d screened Poisson equation for gradient domain problems. In: Computer Vision–ECCV 2008, pp. 114–128 (2008)

    Chapter  Google Scholar 

  19. Xie, Z., Lau, R., Gui, Y., Chen, M., Ma, L.: A gradient-domain-based edge-preserving sharpen filter. In: The Visual Computer, pp. 1–13 (2012)

    Google Scholar 

  20. He, K.M., Sun, J., Tang, X.O.: Guided image filtering. In: Computer Vision–ECCV 2010, vol. 6311, pp. 1–14 (2010)

    Chapter  Google Scholar 

  21. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)

    Article  MATH  Google Scholar 

  22. Alliney, S.: A property of the minimum vectors of a regularizing functional defined by means of the absolute norm. IEEE Trans. Signal Process. 45(4), 913–917 (1997)

    Article  Google Scholar 

  23. Nikolova, M.: A variational approach to remove outliers and impulse noise. J. Math. Imaging Vis. 20(1–2), 99–120 (2004)

    Article  MathSciNet  Google Scholar 

  24. Aujol, J.F., Chambolle, A.: Dual norms and image decomposition models. Int. J. Comput. Vis. 63(1), 85–104 (2005)

    Article  MathSciNet  Google Scholar 

  25. Osher, S.J., Sole, A., Vese, L.A.: Image decomposition and restoration using total variation minimization and the H −1 norm. Multiscale Model. Simul. 1(3), 349–370 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  26. Buades, A., Le, T.M., Morel, J.-M., Vese, L.A.: Fast cartoon + texture image filters. IEEE Trans. Image Process. 19(8), 1978–1986 (2010)

    Article  MathSciNet  Google Scholar 

  27. Peyré, G.: A Numerical Tour of Signal Processing (2010). http://www.ceremade.dauphine.fr/~peyre/numerical-tour

  28. Lev, A., Zucker, S.W., Rosenfeld, A.: Iterative enhancement of noisy images. IEEE Trans. Syst. Man Cybern. 7(6), 435–442 (1977)

    Article  Google Scholar 

  29. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Upper Saddle River (2008)

    Google Scholar 

  30. Petschnigg, G., Szeliski, R., Agrawala, M., Cohen, M., Hoppe, H., Toyama, K.: Digital photography with flash and no-flash image pairs. ACM Trans. Graph. 23(3), 664–672 (2004)

    Article  Google Scholar 

  31. Paris, S., Kornprobst, P., Tumblin, J., Durand, F.: Bilateral filtering: theory and application. In: Computer Graphics and Vision (2008)

    Google Scholar 

  32. Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  33. Xu, L., Lu, C., Xu, Y., Jia, J.: Image Smoothing Via L0 Gradient Minimization SIGGRAPH Asia 2011 p. 2011

  34. Rubinstein, M., Gutierrez, D., Sorkine, O., Shamir, A.: A comparative study of image retargeting. ACM Trans. Graph. 29(5), 160 (2010)

    Google Scholar 

  35. Winnemöller, H., Olsen, S.C., Gooch, B.: Real-time video abstraction. ACM Trans. Graph. 25(3), 1221–1226 (2006)

    Article  Google Scholar 

  36. Zhao, H., Jin, X., Shen, J., Mao, X., Feng, J.: Real-time feature-aware video abstraction. Vis. Comput. 24(7), 727–734 (2008)

    Article  Google Scholar 

  37. Yang, Q.X., Tan, K.H., Ahuja, N.: Real-time o(1) bilateral filtering. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 557–564 (2009)

    Chapter  Google Scholar 

  38. Yang, Q.X., Wang, S.N., Ahuja, N.: Svm for edge-preserving filtering. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010 (2010)

    Google Scholar 

  39. Kass, M., Solomon, J.: Smoothed local histogram filters. ACM Trans. Graph. 29(4), 100 (2010)

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by NSFC-Guangdong Joint Fund (U0935004, U1135003), the National Key Technology R&D Program (2011BAH27B01), the National Science Fund of China (61262050, 61202293), and Ministry of Science and Inovation Subprograme Ramon Y Cajal RYC-2011-09372. Thanks to Antoni Buades, Zeev Farbman, Kaiming He, Michael Kass, Sylvain Paris, Michael Rubinstein, and Kartic Subr for providing their experiments and data in this work.

Author information

Authors and Affiliations

  1. National Engineering Research Center of Digital Life, State-Province Joint Laboratory of Digital Home Interactive Applications, School of Information Science & Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Zhuo Su & Xiaonan Luo

  2. Graphics & Imaging Laboratory (GILab), Universitat de Girona, Edifici P4, Campus Montilivi, Girona, 17071, Spain

    Alessandro Artusi

Authors
  1. Zhuo Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaonan Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alessandro Artusi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhuo Su.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Su, Z., Luo, X. & Artusi, A. A novel image decomposition approach and its applications. Vis Comput 29, 1011–1023 (2013). https://doi.org/10.1007/s00371-012-0753-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-012-0753-5

Keywords

Search

Navigation