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The Visual Computer

, Volume 32, Issue 12, pp 1537–1548 | Cite as

Two-level joint local laplacian texture filtering

  • Hui Du
  • Xiaogang Jin
  • Philip J. Willis
Original Article

Abstract

Extracting the structure component from an image with textures is a challenging problem. This paper presents a novel structure-preserving texture-filtering approach based on the two-level local Laplacian filter. The new texture-filtering method is developed by introducing local Laplacian filters into the joint filtering. Our study shows that local Laplacian filters can also be used for texture smoothing by defining a special remapping function, which is closely related to joint bilateral filtering. This finding leads to a variant of the joint bilateral filter, which produces smooth edges while preserving color variations. Our filter shares similar advantages with the joint bilateral filter, such as being simple to implement and easy to understand. Experiments demonstrate that the new filter can produce satisfactory filtering results with the properties of texture smoothing, smooth edges, and edge shape preserving. We compare our method with the state-of-the-art methods to demonstrate its improvements, and apply this filter to a variety of image-editing applications.

Keywords

Texture filtering Structure extraction  Local Laplacian filters Guidance image 

Notes

Acknowledgments

Xiaogang Jin was supported by the National Natural Science Foundation of China (Grant Nos. 61472351 and 61272298). Hui Du was supported by the Open Project Program of the State Key Lab of CAD&CG (Grant No. A1510), Zhejiang University.

Supplementary material

371_2015_1138_MOESM1_ESM.pdf (37.5 mb)
Supplementary material 1 (pdf 38371 KB)

References

  1. 1.
    Aubry, M., Paris, S., Hasinoff, S.W., Kautz, J., Durand, F.: Fast local Laplacian filters: theory and applications. ACM Trans. Gr. 33(5), 167:1–167:14 (2014)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefzbMATHGoogle Scholar
  3. 3.
    Babaud, J., Witkin, A.P., Baudin, M., Duda, R.O.: Uniqueness of the gaussian kernel for scale-space filtering. IEEE Trans. Pattern Anal. Mach. Intell. 8(1), 26–33 (1986)CrossRefzbMATHGoogle Scholar
  4. 4.
    Bao, L., Song, Y., Yang, Q., Yuan, H., Wang, G.: Tree filtering: efficient structure-preserving smoothing with a minimum spanning tree. IEEE Trans. Image Process. 23(2), 555–569 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    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)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Cho, H., Lee, H., Kang, H., Lee, S.: Bilateral texture filtering. ACM Trans. Gr. 33(4), 128:1–128:8 (2014)CrossRefGoogle Scholar
  7. 7.
    Criminisi, A., Sharp, T., Rother, C., P’erez, P.: Geodesic image and video editing. ACM Trans. Gr. 29(5), 134:1–134:15 (2010)CrossRefGoogle Scholar
  8. 8.
    Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Gr. 27(3), 67:1–67:10 (2008)CrossRefGoogle Scholar
  9. 9.
    Fattal, R.: Edge-avoiding wavelets and their applications. ACM Trans. Gr. 28(3), 1–10 (2009)CrossRefGoogle Scholar
  10. 10.
    Gastal, E.S.L., Oliveira, M.M.: Domain transform for edge-aware image and video processing. ACM Trans. Gr. 30(4), 69:1–69:12 (2011)CrossRefGoogle Scholar
  11. 11.
    He, K., Sun, J., Tang, X.: Guided image filtering. In: Proceedings of the 11th European Conference on Computer Vision, pp. 1–14 (2010)Google Scholar
  12. 12.
    Karacan, L., Erdem, E., Erdem, A.: Structure-preserving image smoothing via region covariances. ACM Trans. Gr. 32(6), 176:1–176:11 (2013)CrossRefGoogle Scholar
  13. 13.
    Kass, M., Solomon, J.: Smoothed local histogram filters. ACM Trans. Gr. 29(4), 100:1–100:10 (2010)CrossRefGoogle Scholar
  14. 14.
    Kopf, J., Cohen, M.F., Lischinski, D., Uyttendaele, M.: Joint bilateral upsampling. ACM Trans. Gr. 26(3), 96:1–96:5 (2007)Google Scholar
  15. 15.
    Lu, C., Xu, L., Jia, J.: Contrast preserving decolorization. In: IEEE International Conference on Computational Photography, pp. 1–7 (2012)Google Scholar
  16. 16.
    Ma, Z., He, K., Wei, Y., Sun, J., Wu, E.: Constant time weighted median filtering for stereo matching and beyond. In: The IEEE International Conference on Computer Vision (ICCV), pp. 49–56 (2013)Google Scholar
  17. 17.
    Paris, S., Hasinoff, S.W., Kautz, J.: Local Laplacian filters: edge-aware image processing with a Laplacian pyramid. ACM Trans. Gr. 30(4), 68:1–68:12 (2011)CrossRefGoogle Scholar
  18. 18.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)CrossRefGoogle Scholar
  19. 19.
    Petschnigg, G., Szeliski, R., Agrawala, M., Cohen, M., Hoppe, H., Toyama, K.: Digital photography with flash and no-flash image pairs. ACM Trans. Gr. 23(3), 664–672 (2004)CrossRefGoogle Scholar
  20. 20.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1–4), 259–268 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Shen, J., Jin, X., Sun, H.: High dynamic range image tone mapping and retexturing using fast trilateral filtering. Vis. Comput. 23(9–11), 641–650 (2007)CrossRefGoogle Scholar
  22. 22.
    Shen, J., Zhao, Y., Yan, S., Li, X.: Exposure fusion using boosting Laplacian pyramid. IEEE Trans. Cybern. 44(9), 1579–1590 (2014)CrossRefGoogle Scholar
  23. 23.
    Zhuo, S., Luo, X., Deng, Z., Liang, Y., Ji, Z.: Edge-preserving texture suppression filter based on joint filtering schemes. IEEE Trans. Multimed. 15(3), 535–548 (2013)CrossRefGoogle Scholar
  24. 24.
    Subr, K., Soler, C., Durand, F.: Edge-preserving multiscale image decomposition based on local extrema. ACM Trans. Gr. 28(5), 147:1–147:9 (2009)CrossRefGoogle Scholar
  25. 25.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision, p. 839C846 (1998)Google Scholar
  26. 26.
    van de Weijer, J., van den Boomgaard, R.: Local mode filtering. In: Computer Vision and Pattern Recognition (CVPR), pp. 428–433 (2001)Google Scholar
  27. 27.
    Weiss, B.: Fast median and bilateral filtering. ACM Trans. Gr. 25(3), 519–526 (2006)CrossRefGoogle Scholar
  28. 28.
    Xu, L., Lu, C., Xu, Y., Jia, J.: Image smoothing via l0 gradient minimization. ACM Trans. Gr. 30(6), 174:1–174:12 (2011)Google Scholar
  29. 29.
    Xu, L., Yan, Q., Xia, Y., Jia, J.: Structure extraction from texture via relative total variation. ACM Trans. Gr. 31(6), 139:1–139:10 (2012)Google Scholar
  30. 30.
    Yin, W., Goldfarb, D., Osher, S.: Image cartoon-texture decomposition and feature selection using the total variation regularized l1 functional. In: Proceedings of the Third International Conference on Variational, Geometric, and Level Set Methods in Computer Vision, pp. 73–84 (2005)Google Scholar
  31. 31.
    Zhang, Q., Shen, X., Xu, L., Jia, J.: Rolling guidance filter. In: Computer Vision—ECCV 2014, pp. 815–830 (2014)Google Scholar
  32. 32.
    Zhang, Q., Xu, L., Jia, J.: 100+ times faster weighted median filter. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2830–2837 (2014)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.New Media College, Zhejiang University of Media and CommunicationsHangzhouChina
  2. 2.State Key Lab of CAD&CG, Zhejiang UniversityHangzhouChina
  3. 3.Department of Computer ScienceUniversity of BathBathUK

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