Skip to main content
SpringerLink
Log in

Fast Global Image Denoising Algorithm on the Basis of Nonstationary Gamma-Normal Statistical Model

  • Conference paper
  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 542))

Abstract

We consider here a Bayesian framework and the respective global algorithm for adaptive image denoising which preserves essential local peculiarities in basically smooth changing of intensity of reconstructed image. The algorithm is based on the special nonstationary gamma-normal statistical model and can handle both Gaussian noise, which is an ubiquitous model in the context of statistical image restoration, and Poissonian noise, which is the most common model for low-intensity imaging used in biomedical imaging. The algorithm being proposed is simple in tuning and has linear computation complexity with respect to the number of image elements so as to be able to process large data sets in a minimal time.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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)

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  3. Yang Q., Tan K.H., Ahuja N.: Real-time O(1) bilateral filtering. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 557–564, Miami (2009)

    Google Scholar 

  4. Tomasi C., Manduchi R.: Bilateral filtering for gray and color images. In: 6th International Conference on Computer Vision, pp. 839–846, Bombay (1998)

    Google Scholar 

  5. Aurich V., Weule J.: Non-linear gaussian filters performing edge preserving diffusion. In: DAGM Symposium, pp. 538–545, Bielefeld (1995)

    Google Scholar 

  6. Smith, S.M., Brady, J.M.: SUSANA new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997)

    Article  Google Scholar 

  7. Elad, M.: On the origin of the bilateral filter and ways to improve it. IEEE Trans. Image Process. 11(10), 1141–1151 (2002)

    Article  MathSciNet  Google Scholar 

  8. Donoho D.L.: Nonlinear wavelet methods for recovery of signals densities and spectra from indirect and noisy data. In: Daubechies, I. (ed.) Different Perspectives on Wavelets, Proceedings of Symposia in Applied Mathematics, vol. 47, pp. 173–205. American Mathematical Society, Providence (1993)

    Google Scholar 

  9. Fryzlewicz, P., Nason, G.P.: A HaarFisz algorithm for poisson intensity estimation. J. Computat. Graph. Stat. 13(3), 621–638 (2004)

    Article  Google Scholar 

  10. Willett, R.M., Nowak, R.D.: Multiscale poisson intensity and density estimation. IEEE Trans. Inf. Theory 53(9), 3171–3187 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  11. Luisier, F., Vonesch, C., Blu, T., Unser, M.: Fast interscale wavelet denoising of poisson-corrupted images. J. Sig. Process. 90, 415–427 (2010)

    Article  MATH  Google Scholar 

  12. Gracheva I., Kopylov A.: Adaptivnyj parametricheskij algoritm sglazhivanija izobrazhenij. Izvestija TulGU, ser. “Tehnicheskie nauki”, Tula: Izd-vo TulGU 9(2), 61–67 (2013) (in Russian)

    Google Scholar 

  13. Markov, M., Mottl, V., Muchnik, I.: Principles of nonstationary regression estimation: a new approach to dynamic multi-factor models in finance. DIMACS Technical report, Rutgers University, USA (2004)

    Google Scholar 

  14. Mottl, V., Blinov, A., Kopylov, A., Kostin, A.: Optimization techniques on pixel neighborhood graphs for image processing. In: Jolion, J.-M., Kropatsch, W.G. (eds.) Graph-Based Representations in Pattern Recognition. Computing Supplement, vol. 12, pp. 135–145. Springer, Wien (1998)

    Chapter  Google Scholar 

Download references

Acknowledgements

This research is funded by RFBR, grant #13-07-00529.

Author information

Authors and Affiliations

  1. Tula State University, Tula, Russian Federation

    Inessa Gracheva, Andrey Kopylov & Olga Krasotkina

Authors
  1. Inessa Gracheva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrey Kopylov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olga Krasotkina
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Inessa Gracheva .

Editor information

Editors and Affiliations

  1. Krasovsky Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Mikhail Yu. Khachay

  2. Wolverhampton, United Kingdom

    Natalia Konstantinova

  3. Technische Universität Darmstadt, Darmstadt, Germany

    Alexander Panchenko

  4. National Research University Higher School of Economics, Moscow, Russia

    Dmitry Ignatov

  5. Ural Federal University, Yekaterinbug, Russia

    Valeri G. Labunets

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Gracheva, I., Kopylov, A., Krasotkina, O. (2015). Fast Global Image Denoising Algorithm on the Basis of Nonstationary Gamma-Normal Statistical Model. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-26123-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26123-2_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26122-5

  • Online ISBN: 978-3-319-26123-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation