Skip to main content
SpringerLink
Log in

Natural image noise removal using nonlocal means and hidden Markov models in transform domain

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Nonlocal means (NLM) which utilizes the self-similarity is considered as one of the most popular denoising techniques. Although NLM can attain significant performance, it shows a few loopholes, such as its computational complexity when it comes to similarity measurements, and the small number of sufficient candidates that use to choose the target patches which have complicated textures. In this paper, the use of clustering based on moment invariants and the hidden Markov model (HMM) is proposed to achieve preclassification and thus capture the dependency between additive white Gaussian noise pixel and its neighbors on the wavelet transform. The HMM also allows hidden states to connect to one another to capture the dependencies among coefficients in the transform domain. In the practical part, the experimental results present that the proposed algorithm has the ability to show denoised images better than the results of state-of-the-art denoising methods both objectively in peak signal-to-noise ratio and structural similarity and subjectively using visual results, especially when the noise level is high.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Khmag, A., Ramli, R., Al-haddad, S.A.R., Kamarudin, N.H., Aqel Mohammad, O.A.: Robust natural image denoising in wavelet domain using hidden Markov models. Indian J. Sci. Technol. 8(32), 1–9 (2015)

    Article  Google Scholar 

  2. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth IEEE International Conference on Computer Vision, pp. 839-846 (1998)

  3. Shao, L., Zhang, H., De Haan, G.: An overview and performance evaluation of classification-based least squares trained filters. IEEE Trans. Image Process. 17(10), 1772–1782 (2008)

    Article  MathSciNet  Google Scholar 

  4. Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60–65 (2005)

  5. Khmag, A., Ramli, A.R., Al-Haddad, S.A.R., Hashim, S.J., Noh, Z.M., Najih, A.A.: Design of natural image denoising filter based on second-generation wavelet transformation and principle component analysis. J. Med. Imaging Health Inform. 5(6), 1261–1266 (2015)

    Article  Google Scholar 

  6. Khmag, A., Ramli, A.R., Al-haddad, S.A.R., Kamarudin, N.: Natural image noise level estimation based on local statistics for blind noise reduction. Vis. Comput. 1–13 (2017) doi:10.1007/00371-017-1362-0

  7. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)

    Article  MathSciNet  Google Scholar 

  8. Mahmoudi, M., Sapiro, G.: Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE Signal Process. Lett. 12(12), 839–842 (2005)

    Article  Google Scholar 

  9. Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imaging 27(4), 425–441 (2008)

    Article  Google Scholar 

  10. Thaipanich, T., Oh, B.T., Wu, P.H., Xu, D., Kuo, C.C.J.: Improved image denoising with adaptive nonlocal means (ANL-means) algorithm. IEEE Trans. Consumer Electron. 56(4), 2623–2630 (2010)

    Article  Google Scholar 

  11. Chen, G., Qian, S.-E.: Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 49(3), 973–980 (2011)

    Article  Google Scholar 

  12. Demir, B., Erturk, S., Gullu, M.: Hyperspectral image classification using denoising of intrinsic mode functions. IEEE Geosci. Remote Sens. Lett. 8(2), 220–224 (2011)

    Article  Google Scholar 

  13. Wang, J., Guo, Y., Ying, Y., Liu, Y., Peng, Q.: Fast non-local algorithm for image denoising. In: IEEE International Conference on Image Processing, pp. 1429–1432 (2006)

  14. Goossens, B., Luong, Q., Pizurica, A., Philips, W.: An improved non-local denoising algorithm. In: International Workshop on Local and Non-Local Approximation in Image Processing, pp. 143–156 (2008)

  15. Xu, G., Kwok, T.H., Wang, C.C.: Isogeometric computation reuse method for complex objects with topology-consistent volumetric parameterization. Comput. Aided Des. 91, 1–13 (2017)

    Article  Google Scholar 

  16. Pang, C., Au, O.C., Dai, J., Yang, W., Zou, F.: A fast NL-means method in image denoising based on the similarity of spatially sampled pixels. In: IEEE International Workshop on Multimedia Signal Processing, pp. 1–4 (2009)

  17. Khmag, A., Ramli, A.R., Al-haddad, S.A.R., Yusoff, S., Kamarudin, N.H.: Denoising of natural images through robust wavelet thresholding and genetic programming. Vis. Comput. 33(9), 1141–1154

    Article  Google Scholar 

  18. Khmag, A., Ramli, A.R., Hashim, S.J., Al-Haddad, S.A.R.: Additive noise reduction in natural images using second-generation wavelet transform hidden Markov models. IEEJ Trans. Electr. Electron. Eng. 11(3), 339–347 (2016)

    Article  Google Scholar 

  19. Grewenig, S., Zimmer, S., Weickert, J.: Rotationally invariant similarity measures for nonlocal image denoising. J. Vis. Commun. Image Represent. 22(2), 117–130 (2011)

    Article  Google Scholar 

  20. Yan, R.: Adaptive Representations for Image Restoration. PhD Thesis, University of Sheffield, UK (2014)

  21. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  22. Salmon, J.: On two parameters for denoising with non-local means. IEEE Signal Process. Lett. 17(3), 269–272 (2010)

    Article  Google Scholar 

  23. Yan, R., Shao, L., Cvetkovic, S.D., Klijn, J.: Improved nonlocal means based on pre-classification and invariant block matching. IEEE/OSA J. Disp. Technol. 8(4), 212–218 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Zawia University, Zawiya, Libya

    Asem Khmag

  2. Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang, Malaysia

    S. A. R. Al Haddad, R. A. Ramlee & Noraziahtulhidayu Kamarudin

  3. Faculty of Computer Science, Cihan University, Sulaimaniya, Iraq

    Fahad Layth Malallah

Authors
  1. Asem Khmag
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. A. R. Al Haddad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. A. Ramlee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Noraziahtulhidayu Kamarudin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fahad Layth Malallah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Asem Khmag.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khmag, A., Al Haddad, S.A.R., Ramlee, R.A. et al. Natural image noise removal using nonlocal means and hidden Markov models in transform domain. Vis Comput 34, 1661–1675 (2018). https://doi.org/10.1007/s00371-017-1439-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-017-1439-9

Keywords

Search

Navigation