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Signal, Image and Video Processing

, Volume 12, Issue 6, pp 1165–1172 | Cite as

Propagated guided image filtering for edge-preserving smoothing

  • J. Mun
  • Y. Jang
  • J. Kim
Original Paper
  • 169 Downloads

Abstract

This paper proposes an edge-preserving smoothing filtering algorithm based on guided image filter (GF). GF is a well-known edge-preserving smoothing filter, but is ineffective in certain cases. The proposed GF enhancement provides a better solution for various noise levels associated with image degradation. In addition, halo artifacts, the main drawback of GF, are well suppressed using the proposed method. In our proposal, linear GF coefficients are updated sequentially in the spatial domain by using a new cost function, whose solution is a weighted average of the neighboring coefficients. The weights are determined differently depending on whether the pixels belong to the edge region, and become zero when a neighborhood pixel is located within a region separated from the center pixel. This propagation procedure is executed twice (from upper-left to lower-right, and vice versa) to obtain noise-free edges. Finally, the filtering output is computed using the updated coefficient values. The experimental results indicate that the proposed algorithm preserves edges better than the existing algorithms, while reducing halo artifacts even in highly noisy images. In addition, the algorithm is less sensitive to user parameters compared to GF and other modified GF algorithms.

Keywords

Smoothing EPS Filtering Guided image filtering 

Notes

Acknowledgements

This material is based upon work supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program (10080619). The authors would like to thank anonymous reviewers for the comments.

References

  1. 1.
    Wei, D., Li, Y.M.: Generalized Sampling Expansions with Multiple Sampling Rates for Lowpass and Bandpass Signals in the Fractional Fourier Transform Domain. IEEE Trans. Signal Process. 64(18), 4861–4874 (2016)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Wei, D.: Image super-resolution reconstruction using the high-order derivative interpolation associated with fractional filter functions. IET Signal Process. 10(9), 1052–1061 (2016)CrossRefGoogle Scholar
  3. 3.
    Wei, D., Li, Y.: Reconstruction of multidimensional bandlimited signals from multichannel samples in linear canonical transform domain. IET Signal Process. 8(6), 647–657 (2014)CrossRefGoogle Scholar
  4. 4.
    Kim, B., Park, R., Chang, S.: Tone mapping with contrast preservation and lightness correction in high dynamic range imaging. SIViP 10(8), 1425–1432 (2016)CrossRefGoogle Scholar
  5. 5.
    Winnemller, H., Olsen, S.C., Gooch, B.: Real-time video abstraction. ACM Trans. Graph. 25(3), 1221–1226 (2006)CrossRefGoogle Scholar
  6. 6.
    Miao, J., Chu, M., Zhang, G., Zhang, M.: Disparity map optimization using sparse gradient measurement under intensity-edge constraints. SIViP 10(1), 161–169 (2016)CrossRefGoogle Scholar
  7. 7.
    He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)CrossRefGoogle Scholar
  8. 8.
    Xu, L., Lu, C., Xu, Y., Jia, J.: Image smoothing via L0 gradient minimization. ACM Trans. Graph. 30(6), 174–184 (2011)Google Scholar
  9. 9.
    Min, D., Choi, S., Lu, J., Ham, B., Sohn, K., Do, M.N.: Fast global image smoothing based on weighted least squares. IEEE Trans. Image Process. 23(12), 5638–5653 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Xu, L., Yan, Q., Xia, Y., Jia, J.: Structure extraction from texture via relative total variation. ACM Trans. Graph. 31(6), 139 (2012)Google Scholar
  11. 11.
    Bi, S., Xiaoguang, H., Yizhou, Y.: An L1 image transform for edge-preserving smoothing and scene-level intrinsic decomposition. ACM Trans. Graph. 34(4), 78 (2015)CrossRefzbMATHGoogle Scholar
  12. 12.
    Tang, C., Hou, C., Hou, Y., Wang, P., Li, W.: An effective edge-preserving smoothing method for image manipulation. Digit. Signal Process. 63, 10–24 (2017)CrossRefGoogle Scholar
  13. 13.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of IEEE International Conference on Computer Vision, pp. 839–846 (1998)Google Scholar
  14. 14.
    Petschnigg, G., Agrawala, M., Hoppe, H., Szeliski, R., Cohen, M., Toyama, K.: Digital photography with flash and no-flash image pairs. ACM Trans. Graph. 23(3), 664–672 (2004)CrossRefGoogle Scholar
  15. 15.
    Gastal, E.S., Oliveira, M.M.: Domain transform for edge-aware image and video processing. In: ACM Transactions on Graphics, vol. 30, no. 4 (2011)Google Scholar
  16. 16.
    Li, Z., Zheng, J., Zhu, Z., Yao, W., Wu, S.: Weighted guided image filtering. IEEE Trans. Image Process. 24(1), 120–129 (2015)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Kou, F., Chen, W., Wen, C., Li, Z.: Gradient domain guided image filtering. IEEE Trans. Image Process. 24(11), 4528–4539 (2015)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Sciacchitano, F., Dong, Y., Zeng, T.: Variational approach for restoring blurred images with Cauchy noise. SIAM J. Imaging Sci. 8(3), 1894–1922 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: TID2008 a database for evaluation of full-reference visual quality assessment metrics. Adv. Mod. Radioelectron. 10(4), 3045 (2009)Google Scholar
  20. 20.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  21. 21.
    Sattar, F., Floreby, L., Salomonsson, G., Lovstrom, B.: Image enhancement based on a nonlinear multiscale method. IEEE Trans. Image Process. 6(6), 888895 (1997)CrossRefGoogle Scholar
  22. 22.
    Huynh-Thu, Q., Mohammed, G.: Scope of validity of PSNR in image/video quality assessment. Electron. Lett. 44(13), 800–801 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and Electronic EngineeringYonsei UniversitySeoulKorea

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