A Novel Blind Image Restoration Algorithm Using A SVR-Based Noise Reduction Technique

  • You Sheng Xia
  • Shi Quan Bin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)


In many applications, the received image is degraded by unknown blur and noise. Traditional blind image deconvolution algorithms have drawback of noise amplification. For robustness against the influence of noise, this paper proposes a novel blind image deconvolution algorithm by combining the support vector regression (SVR) approach and the total variation approach. The proposed algorithm has a lower computational complexity and a good performance in image denoising and image deblurring. Illustrative examples show that the proposed blind image deconvolution algorithm and has better performance in improvement signal-to-noise ratio than two traditional blind image restoration algorithms.


Blind image restoration Noise reduction Support vector regression Total variation approach 



This work is supported in part by the National Natural Science Foundation of China under Grant No. 61179037.


  1. 1.
    Andrews HC, Hunt BR (1977) Digital image restoration. Prentice-Hall, New YorkGoogle Scholar
  2. 2.
    Aujol JE, Gilboa G, Chan T, Osher S (2006) Structure,-texture image decomposition modeling, algorithms, and parameter selection. Int J Comput Vision 67:111–136CrossRefMATHGoogle Scholar
  3. 3.
    Kundur D, Hatzinakos D (1996) Blind image deconvolution. IEEE Signal Process Mag 13:43CrossRefGoogle Scholar
  4. 4.
    Campisi P, Egiazarian K (2007) Blind image deconvolution: theory and applications. CRC Press, Boca RatonGoogle Scholar
  5. 5.
    Tekalp AM, Kaufman H, Woods JW (1986) Identification of image and blur parameters for the restoration of noncausal blurs. IEEE Trans Acoust Speech Signal Process 34:963–968Google Scholar
  6. 6.
    Kundur D, Hatzinakos D (1998) A novel blind deconvolution scheme for image restoration using recursive filtering. IEEE Trans Signal Process 46:375CrossRefMathSciNetGoogle Scholar
  7. 7.
    You Y-L, Kaveh M (1996) A regularization approach to joint blur identification and image restoration. IEEE Trans Image Process 5:416CrossRefGoogle Scholar
  8. 8.
    Chan TF, Wong C-K (1998) Total variation blind deconvolution. IEEE Trans Image Process 7:370CrossRefGoogle Scholar
  9. 9.
    He L, Marquina A, OSher SJ (2005) Blind deconvolution using TV regularization and Bregman iteration, Int J Imaging Syst Technol 15:74–83Google Scholar
  10. 10.
    Chen L, Yap KH (2005) A soft double regularization approach to parametric blind image deconvolution. IEEE Trans Image Process 14:624–633CrossRefGoogle Scholar
  11. 11.
    Chow TWS , Li X-D, Ng K-T (2001) Double-regularization approach for blind restoration of multichannel imagery. In: IEEE transactions. Circuits and systems I:fundantmental theory and applications 48:1075Google Scholar
  12. 12.
    Jinglian Z, Xia YS (2010) A two-dimensional algorithm for blind image restoration using a novel mixed L1 regularization approach. In: International congress on image and signal processing, Yantai, ChinaGoogle Scholar
  13. 13.
    Sroubek F, Flusser J (2003) Multichannel blind iterative image restoration. IEEE Trans Image Process 12:1094–1106Google Scholar
  14. 14.
    Dalong L, Russell MM, Steven S (2005) Blind image deconvolution using support vector regression. ICASSP 2:113–116Google Scholar
  15. 15.
    Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Phys D 60:259–268Google Scholar
  16. 16.
    Gilboa G, Zeevi YY, Sochen N (2003) Texture preserving variational denoising using an adaptive fidelity term. In: Proceedings of the VLSMGoogle Scholar
  17. 17.
    Liu J, Huan Z, Huang H, Zhang H (2009) An adaptive method for recovering image from mixed noisy data. Int J Comput Vis 85:182–191Google Scholar
  18. 18.
    Strong DM, Chan TF (1996) Spatially and scale adaptive total variation based regularization and anisotropic diffusion in image processing. UCLA CAM Report 96–46Google Scholar
  19. 19.
    Ghouti L, Bouridane A (2005) Two-step variance-adaptive image denoising. In: Proceedings of the ICIP2005Google Scholar
  20. 20.
    Hashemi M, Beheshti S (2010) Adaptive noise variance estimation in BayesShrink. IEEE signal process lett 17(1):12–15Google Scholar
  21. 21.
    Chang SG, Yu B, Vetterli M (2000) Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process 9(9):1532–1546Google Scholar
  22. 22.
    Chang SG, Bin Y, Vetterli M (2000) Spatially adaptive wavelet thresholding with context modeling for image denoising. IEEE Trans Image Process 9(9):1522–1531Google Scholar
  23. 23.
    Hamza A, Krim H (2001) Image denoising: A nonlinear robust statistical approach. IEEE Trans Signal Process 49(12):3045–3054Google Scholar
  24. 24.
    Hou Z, Koh TS (2004) Image denoising using robust regression. IEEE Signal Process Lett 11(2):243–246Google Scholar
  25. 25.
    Rabie T (2005) Robust estimation approach for blind denoising. IEEE Trans Image Process 14(11):1755–1765Google Scholar
  26. 26.
    Kulkarni RK, Meher S, Nair JM (2010) An algorithm for image denoising by robust estimator. Eur J Sci Res ISSN 1450–216X 39(3):372–380Google Scholar
  27. 27.
    Dalong L (2009) Support vector regression based image denoising. Image Vis Comput 27:623–627CrossRefGoogle Scholar
  28. 28.
    Wang Y, Cheung Y, Liu H (eds) (2007) Image denoising based on wavelet support vector machine. CIS 2006, LNAI 4456, pp 963–971Google Scholar
  29. 29.
    Zeng G, Zhao R (2007) Image denoising using least squares wavelet support vector machines. Chin Opt Lett 5(11):632–635Google Scholar
  30. 30.
    Vapnik V (1995) The nature of statistical learning theory. Springer, New YorkGoogle Scholar
  31. 31.
    Wu HQ, Xia YS (2011) A simplified SVR method for blind image denoising. In: International congress on image and signal processing. Shanghai, China, pp 47–50Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.College of Mathematics and Computer ScienceFuzhou UniversityFuzhouChina

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