Skip to main content
Log in

Adaptive fourth-order diffusion smoothing via bilateral kernel

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

This paper presents a flexible and adaptive fourth-order diffusion model for image noise removal. A flexible fourth-order diffusion equation is first presented. Then, an efficient and robust contextual discontinuity indicator based on the bilateral kernel is constructed and combined with the local discontinuity indicator (local spatial gradient) to get the desired adaptive fourth-order diffusion model. The proposed model degenerates to an anisotropic equation which performs the tangent smoothing for sharpening the inter-object boundaries and reduces to an isotropic equation which implements the fast smoothing in the homogenous regions for noise removal. Moreover, it is able to overcome the staircase artifacts arisen by most of second-order diffusion models. Experimental results are provided to validate the effectiveness of the proposed model with regard to objective evaluation metrics and visual performance which outperform those of some benchmark models.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of IEEE International Conference on Computer Vision, India, pp. 839–846 (1998)

  2. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse \(3\)-D transform domain collaborative filtering. IEEE Trans. Image Process. 16, 2080–2095 (2007)

    Article  MathSciNet  Google Scholar 

  3. Zuo, W.M., Zhang, L., Song, C.W., Zhang, D., Gao, H.J.: Gradient histogram estimation and preservation for texture enhanced image denoising. IEEE Trans. Image Process. 23, 2459–2472 (2014)

    Article  MathSciNet  Google Scholar 

  4. Zhang, K., Zuo, W.M., Chen, Y.J., Meng, D.Y., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26, 3142–3155 (2017)

    Article  MathSciNet  Google Scholar 

  5. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  7. You, Y.L., Xu, W., Tannenbaum, A., Kaveh, M.: Behavioral analysis of anisotropic diffusion in image processing. IEEE Trans. Image Process. 5, 1539–1553 (1996)

    Article  Google Scholar 

  8. Gilboa, G., Sochen, N., Zeevi, Y.Y.: Image enhancement and denoising by complex diffusion processes. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1020–1036 (2004)

    Article  Google Scholar 

  9. Chen, K.: Adaptive smoothing via contextual and local discontinuities. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1552–1567 (2005)

    Article  Google Scholar 

  10. Wang, Y., Zhang, L., Li, P.: Local variance-controlled forward-and-backward diffusion for image enhancement and noise reduction. IEEE Trans. Image Process. 16, 1854–1864 (2007)

    Article  MathSciNet  Google Scholar 

  11. Chen, Q., Montesinos, P., Sun, Q.S., Xia, D.S.: Ramp preserving Perona–Malik model. Signal Process. 90, 1963–1975 (2010)

    Article  Google Scholar 

  12. Guo, Z.C., Sun, J.B., Zhang, D.Z., Wu, B.Y.: Adaptive Perona–Malik model based on the variable exponent for image denoising. IEEE Trans. Image Process. 21, 958–967 (2012)

    Article  MathSciNet  Google Scholar 

  13. Nadernejad, E., Sharifzadeh, S., Forchhammer, S.: Using anisotropic diffusion equations in pixon domain for image denoising. Signal Image Video Process. 7, 1113–1124 (2013)

    Article  Google Scholar 

  14. Prasath, V.B.S., Vorotnikov, D., Pelapur, R., Jose, S., Seetharaman, G., Palaniappan, K.: Multiscale Tikhonov-total variation image restoration using spatially varying edge coherence exponent. IEEE Trans. Image Process. 24, 5220–5235 (2015)

    Article  MathSciNet  Google Scholar 

  15. Xu, J.T., Jia, Y.Y., Shi, Z.F., Pang, K.: An improved anisotropic diffusion filter with semi-adaptive threshold for edge preservation. Signal Process. 119, 80–91 (2016)

    Article  Google Scholar 

  16. Rial, R.M., Herrero, J.M.: Separable anisotropic diffusion. Int. J. Comput. Vis. 126, 651–670 (2018)

    Article  MathSciNet  Google Scholar 

  17. You, Y.L., Kaveh, M.: Fourth-order partial differential equations for noise removal. IEEE Trans. Image Process. 9, 1723–1730 (2000)

    Article  MathSciNet  Google Scholar 

  18. Lysaker, M., Tai, X.C.: Iterative image restoration combining total variation minimization and a second-order functional. Int. J. Comput. Vis. 66, 5–18 (2006)

    Article  Google Scholar 

  19. Papafitsoros, K., Schönlieb, C.B.: A combined first and second order variational approach for image reconstruction. J. Math. Imaging Vis. 48, 308–338 (2014)

    Article  MathSciNet  Google Scholar 

  20. Yang, J.H., Zhao, X.L., Mei, J.J., Wang, S., Ma, T.H., Huang, T.Z.: Total variation and high-order total variation adaptive model for restoring blurred images with Cauchy noise. Comput. Math. Appl. 77, 1255–1272 (2019)

    Article  MathSciNet  Google Scholar 

  21. Hajiaboli, M.R.: A self-governing hybrid model for noise removal. Lect. Notes Comput. Sci. 5414, 295–305 (2009)

    Article  Google Scholar 

  22. Bredies, K., Kunisch, K., Pock, T.: Total generalized variation. SIAM J. Imaging Sci. 3, 492–526 (2010)

    Article  MathSciNet  Google Scholar 

  23. Hajiaboli, M.R.: An anisotropic fourth-order diffusion filter for image noise removal. Int. J. Comput. Vis. 92, 177–191 (2011)

    Article  MathSciNet  Google Scholar 

  24. Hu, Y., Jacob, M.: Higher degree total variation (HDTV) regularization for image recovery. IEEE Trans. Image Process. 21, 2559–2571 (2012)

    Article  MathSciNet  Google Scholar 

  25. Kang, M., Jung, M.: Higher-order regularization based image restoration with automatic regularization parameter selection. Comput. Math. Appl. 76, 58–80 (2018)

    Article  MathSciNet  Google Scholar 

  26. Lefkimmiatis, S., Bourquard, A., Unser, M.: Hessian-based norm regularization for image restoration with biomedical applications. IEEE Trans. Image Process. 21, 983–995 (2012)

    Article  MathSciNet  Google Scholar 

  27. Siddig, A., Guo, Z.C., Zhou, Z.Y., Wu, B.Y.: An image denoising model based on a fourth-order nonlinear partial differential equation. Comput. Math. Appl. 76, 1056–1074 (2018)

    Article  MathSciNet  Google Scholar 

  28. Chan, T., Marquina, A., Mulet, P.: High-order total variation-based image restoration. SIAM J. Sci. Comput. 22, 503–516 (2000)

    Article  MathSciNet  Google Scholar 

  29. Lysaker, M., Lundervold, A., Tai, X.C.: Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Trans. Image Process. 12, 1579–1590 (2003)

    Article  Google Scholar 

  30. Li, F., Shen, C., Fan, J., Shen, C.: Image restoration combining a total variational filter and a fourth-order filter. J. Vis. Commun. Image Represent. 18, 322–330 (2007)

    Article  Google Scholar 

  31. Didas, S., Setzer, S., Steidl, G.: Combined \(l_{2}\) data and gradient fitting in conjunction with \(l_{1}\) regularization. Adv. Comput. Math. 30, 79–99 (2009)

    Article  MathSciNet  Google Scholar 

  32. Liu, X., Huang, L., Guo, Z.: Adaptive fourth-order partial differential equation filter for image denoising. Appl. Math. Lett. 24, 1282–1288 (2011)

    Article  MathSciNet  Google Scholar 

  33. Liu, X.Y., Lai, C.H., Pericleous, K.A.: A fourth-order partial differential equation denoising model with an adaptive relaxation method. Int. J. Comput. Math. 92, 608–622 (2014)

    Article  MathSciNet  Google Scholar 

  34. Zeng, W., Lu, X., Tan, X.: A local structural adaptive partial differential equation for image denoising. Multimed. Tools Appl. 74, 743–757 (2015)

    Article  Google Scholar 

  35. Jia, T.T., Shi, Y.Y., Zhu, Y.G., Wang, L.: An image restoration model combining mixed \(L^{1}/L^{2}\) fidelity terms. J. Vis. Commun. Image Represent. 38, 461–473 (2016)

    Article  Google Scholar 

  36. Zhang, X.J., Ye, W.Z.: An adaptive fourth-order partial differential equation for image denoising. Comput. Math. Appl. 74, 2529–2545 (2017)

    Article  MathSciNet  Google Scholar 

  37. Deng, L.Z., Zhu, H., Yang, Z., Li, Y.J.: Hessian matrix-based fourth-order anisotropic diffusion filter for image denoising. Opt. Laser Technol. 110, 184–190 (2019)

    Article  Google Scholar 

  38. Zhang, K., Zuo, W.M., Chen, Y.J., Meng, D.Y., Zhang, L.: Set 12. GitHub. https://www.github.com/cszn/DnCNN. Accessed 13 Aug 2016

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

    Article  Google Scholar 

  40. Mittal, A., Soundararajan, R., Bovik, A.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20, 209–212 (2013)

    Article  Google Scholar 

  41. Aubert, G., Aujol, J.F.: A variational approach to removing multiplicative noise. SIAM J. Appl. Math. 68, 925–946 (2008)

    Article  MathSciNet  Google Scholar 

  42. Borzì, A., Bisceglie, M.D., Galdi, C., Pallotta, L., Ullo, S.L.: Phase retrieval in SAR interferograms using diffusion and inpainting. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, Honolulu, pp. 2912–2915 (2010)

  43. Ullo, S.L., Addabbo, P., Martire, D.D., Sica, S., Fiscante, N., Cicala, L., Angelino, C.V.: Application of DInSAR technique to high coherence sentinel-1 images for dam monitoring and result validation through in situ measurements. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12, 875–890 (2019)

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the anonymous referees for their valuable comments that have led to a greatly improved paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong Chen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The work was supported by Key Program from Data Recovery Key Laboratory of Sichuan Province (Grant No. DRN19013).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., Gao, Y. Adaptive fourth-order diffusion smoothing via bilateral kernel. SIViP 15, 1125–1133 (2021). https://doi.org/10.1007/s11760-020-01839-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-020-01839-5

Keywords

Navigation