Advertisement

An Improved Weighted-Least-Squares-Based Method for Extracting Structure from Texture

  • Qing Zuo
  • Lin Dai
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

Extracting meaningful structures from textured images is an import operation for further image processings such as tone mapping, detail enhancement and pattern recognition. Researchers have pay attention to this topic for decades and developed different techniques. However, though some existing methods can generate satisfying results, they are not fast enough for realtimely handling moderate images (with resolution \(1920\times 1080\times 3\)). In this paper, we propose a novel variational model based on weighted least square and a very fast solver which can be highly parallelized on GPUs. Experiments have shown our method is possible to operate images with resolution \(1920\times 1080\times 3\) realtimely.

Keywords

Texture Structure Weighted least squares GPU 

Notes

Acknowledgment

This paper is supported by the Post-Doctoral Research Center of China Digital Video (Beijing) Limited.

References

  1. 1.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision, ICCV 1998, Washington, DC, USA, p. 839. IEEE Computer Society (1998)Google Scholar
  2. 2.
    Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph. 21(3), 257–266 (2002)CrossRefGoogle Scholar
  3. 3.
    Gunturk, B.K.: Fast bilateral filter with arbitrary range and domain kernels. IEEE Trans. Image Process. A Publ. IEEE Sign. Process. Soc. 20(9), 2690–2696 (2011)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chaudhury, K.N., Sage, D., Unser, M.: Fast \(O\)(1) bilateral filtering using trigonometric range kernels. IEEE Trans. Image Process. A Publ. IEEE Sign. Process. Soc. 20(12), 3376 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Cho, H., Lee, H., Kang, H., Lee, S.: Bilateral texture filtering. ACM Trans. Graph. 33(4), 128:1–128:8 (2014)CrossRefGoogle Scholar
  7. 7.
    Zhang, F., Dai, L., Xiang, S., Zhang, X.: Segment graph based image filtering: fast structure-preserving smoothing. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), ICCV 2015, Washington, DC, USA, pp. 361–369. IEEE Computer Society (2015)Google Scholar
  8. 8.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D 60(1–4), 259–268 (1992)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Aujol, J.F., Gilboa, G., Chan, T., Osher, S.: Structure-texture image decompositionmodeling, algorithms, and parameter selection. Int. J. Comput. Vis. 67(1), 111–136 (2006)CrossRefGoogle Scholar
  10. 10.
    Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph. 27(3), 1–10 (2008)CrossRefGoogle Scholar
  11. 11.
    Xu, L., Yan, Q., Xia, Y., Jia, J.: Structure extraction from texture via relative total variation. ACM Trans. Graph. 31(6), 139:1–139:10 (2012)Google Scholar
  12. 12.
    Tan, X., Sun, C., Pham, T.D.: Multipoint filtering with local polynomial approximation and range guidance. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2941–2948 (2014)Google Scholar
  13. 13.
    Ham, B., Cho, M., Ponce, J.: Robust image filtering using joint static and dynamic guidance. In: Computer Vision and Pattern Recognition, pp. 4823–4831 (2015)Google Scholar
  14. 14.
    Hadjidimos, A.: Successive overrelaxation (SOR) and related methods. J. Comput. Appl. Math. 123(1), 177–199 (2000). Numerical Analysis 2000. Vol. III: Linear AlgebraMathSciNetCrossRefGoogle Scholar
  15. 15.
  16. 16.
    Min, D., Choi, S., Jiangbo, L., Ham, B., Sohn, K., Minh, N.D.: Fast global image smoothing based on weighted least squares. IEEE Trans. Image Process. 23(12), 5638 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.China Digital Video (Beijing) LimitedBeijingChina

Personalised recommendations