Advertisement

A Novel Image Denoising Algorithm Based on Non-subsampled Contourlet Transform and Modified NLM

  • Huayong Yang
  • Xiaoli Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

Abstract

A novel image denoising algorithm based on non-subsampled contourlet transform (NSCT) and modified non-local mean (NLM) is proposed. First, we utilize NSCT to decompose the images to obtain the high frequency coefficients. Second, the high frequency coefficients are used for modified NLM denoising. Finally, the NLM weight values are calculated by modified bisquare function instead of Gaussian kernel function of the traditional NLM, and each noise coefficient is corrected to get the denoised image. According to results of the simulation experiment, the denoising results of the proposed algorithm obtain higher peak signal-to-noise ratio (PSNR) and better retains structural information of image in subjective vision.

Keywords

Non-subsampled contourlet transform (NSCT) Non-local mean (NLM) Denoising 

Notes

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (No. 61502356), by Hubei Province Natural Science Foundation of China (No. 2018CFB526).

References

  1. 1.
    Luo, E., Chan, S.H., Nguyen, T.Q.: Adaptive image denoising by targeted databases. IEEE Trans. Image Process. 24(7), 2167–2181 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Chen, F., Zeng, X., Wang, M.: Image denoising via local and nonlocal circulant similarity. J. Vis. Commun. Image Represent. 30(3), 117–124 (2015)CrossRefGoogle Scholar
  3. 3.
    Schmidt, U., Gao, Q., Roth, S.: A generative perspective on MRFs in low-level vision. In: Proceedings of 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , San Francisco, CA, pp. 1751–1758 (2010)Google Scholar
  4. 4.
    Zhang, W.: Image denoising algorithm of refuge chamber by combining wavelet transform and bilateral filtering. Int. J. Min. Sci. Technol. 23(2), 221–225 (2013)CrossRefGoogle Scholar
  5. 5.
    Cho, S.I., Kang, S.J., Kim, H.S.: Dictionary-based anisotropic diffusion for noise reduction. Pattern Recogn. Lett. 46(3), 36–45 (2014)CrossRefGoogle Scholar
  6. 6.
    Hill, P.R., Achim, A.M., Bull, D.R.: Dual-tree complex wavelet coefficient magnitude modelling using the bivariate Cauchy-Rayleigh distribution for image denoising. Sig. Process. 105(12), 464–472 (2014)CrossRefGoogle Scholar
  7. 7.
    Liu, X.M., Tian, Y., He, H.: Improved non-local means algorithm for image denoising. Comput. Eng. 38(4), 199–207 (2012)Google Scholar
  8. 8.
    Zhong, H., Ma, K., Zhou, Y.: Modified BM3D algorithm for image denoising using nonlocal centralization prior. Sig. Process. 106(8), 342–347 (2015)CrossRefGoogle Scholar
  9. 9.
    Nguyen, M.P., Chun, S.Y.: Bounded self-weights estimation method for non-local means image denoising using minimax Estimators. IEEE Trans. Image Process. 26(4), 1637–1649 (2017)MathSciNetCrossRefGoogle Scholar
  10. 10.
    DaCunha, A.L., Zhou, J., Do, M.N.: The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans. Image Process. 15(10), 3089–3101 (2006)CrossRefGoogle Scholar
  11. 11.
    Goossens, B., Luong, Q., Pizurica, A.: An improved non-local denoising algorithm. In: International Workshop on Local and Non-local Approximation in Image Processing, Switzerland, pp. 143–156 (2008)Google Scholar
  12. 12.
    Shao, L., Yan, R., Li, X.: From heuristic optimization to dictionary learning: a review and comprehensive comparison of image denoising algorithms. IEEE Trans. Cybern. 44(7), 1001–1013 (2014)CrossRefGoogle Scholar
  13. 13.
    Shao, L., Zhang, H., Haan, G.: An overview and performance evaluation of classification based least squares trained filters. IEEE Trans. Image Process. 17(10), 1772–1782 (2008)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Zhu, X., Milanfar, P.: Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Trans. Image Process. 19(12), 3116–3132 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Bouboulis, P., Slavakis, K., Theodoridis, S.: Adaptive kernel-based image denoising employing semiparametric regularization. IEEE Trans. Image Process. 19(6), 1465–1479 (2010)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), USA, CA, San Diego, pp. 60–65 (2005)Google Scholar
  17. 17.
    Goossens, B., Luong, H., Pizurica, A., Philips, W.: An improved non-local denoising algorithm. In: International Workshop on Local Non-local Approximation Image Processing, pp. 143–156 (2008)Google Scholar
  18. 18.
    Yang, H.Y., Wang, X.Y., Niu, P.P.: Image denoising using nonsubsampled shearlet transform and twin support vector machines. Neural Netw. 57(9), 152–165 (2014)CrossRefGoogle Scholar
  19. 19.
    Portilla, J., Strela, V., Wainwright, M.J., Simoncelli, E.P.: Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans. Image Process. 12(11), 1338–1351 (2003)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Pizurica, A., Philips, W.: Estimating the probability of the presence of a signal of interest in multi-resolution single-and multi-band image denoising. IEEE Trans. Image Process. 15(3), 654–665 (2006)CrossRefGoogle Scholar
  21. 21.
    Luisier, F., Blu, T., Unser, M.: A new SURE approach to image denoising: interscale orthonormal wavelet thresholding. IEEE Trans. Image Process. 16(3), 593–606 (2007)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D 60(1–4), 259–268 (1992)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information EngineeringCity College of Wuhan University of Science and TechnologyWuhanChina
  2. 2.School of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina

Personalised recommendations