Selective Parameters Based Image Denoising Method

  • Mantosh Biswas
  • Hari Om
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 182)


In this paper, we propose a Selective Parameters based Image Denoising method that uses a shrinkage parameter for each coefficient in the subband at the corresponding decomposition level. Image decomposition is done using the wavelet transform. VisuShrink, SureShrink, and BayesShrink define good thresholds for removing the noise from an image. SureShrink and BayesShrink denoising methods depend on subband to evaluate the threshold value whereas the VisuShrink is a global thresholding method. These methods remove too many coefficients and do not provide good visual quality of the image. Our proposed method not only keeps more noiseless coefficients but also modifies the noisy coefficients using the threshold value. We experimentally show that our method provides better performance in terms of objective and subjective criteria i.e. visual quality of image than the VisuShrink, SureShrink, and BayesShrink.


Image denoising Wavelet coefficient Thresholding Peak-Signal-to-Noise Ratio (PSNR) 


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  1. 1.
    Jansen, M.: Noise Reduction by Wavelet Thresholding. Springer, New York (2001)MATHCrossRefGoogle Scholar
  2. 2.
    Xie, J.C.: Overview on Wavelet Image Denoising. Journal of Image and Graphic 7(3), 209–217 (2002)Google Scholar
  3. 3.
    Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation via wavelet shrinkage. Biometrika 81, 425–455 (1994)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Donoho, D.L., Johnstone, I.M.: Wavelet shrinkage: Asymptotic? J. R. Stat. Soc. B 57(2), 301–369 (1995)MathSciNetMATHGoogle Scholar
  5. 5.
    Donoho, D.L., Johnstone, I.M.: Adapting to Unknown Smoothness via Wavelet Shrinkage. Journal of American Statistical Association 90(432), 1200–1224 (1995)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Donoho, D.L.: De-Noising by Soft Thresholding. IEEE Trans. Info. Theory 41(3), 613–627 (1995)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Chang, S.G., Yu, B., Vetterli, M.: Adaptive Wavelet Thresholding for Image De-noising and Compression. IEEE Trans. Image Processing 9(9), 1532–1546 (2000)MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    Elyasi, I., Zarmehi, S.: Elimination Noise by Adaptive Wavelet Threshold. World Academy of Science, Engineering and Technology, 462–466 (2009)Google Scholar
  9. 9.
    Weeks, M., Bayoumi, M.: Discrete Wavelet Transform: Architectures, Design and Performance Issues. Journal of VLSI Signal Processing 35(2), 155–178 (2003)MATHCrossRefGoogle Scholar
  10. 10.
    Daubechies, I.: The Wavelet Transform, Time-Frequency Localization and Signal Analysis. IEEE Transaction on Information Theory 36(5), 961–1005 (1990)MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Yang, Y., Wei, Y.: Neighboring Coefficients Preservation for Signal Denoising. Circuits, Systems, and Signal Processing 31(2), 827–832 (2012)MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Om, H., Biswas, M.: An Improved Image Denoising Method based on Wavelet Thresholding. Journal of Signal and Information Processing 3(1), 109–116 (2012)CrossRefGoogle Scholar
  13. 13.
    Chen, G., Zhu, W.-P.: Image Denoising Using Neighbouring Contourlet Coefficients. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds.) ISNN 2008, Part II. LNCS, vol. 5264, pp. 384–391. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringIndian School of MinesDhanbadIndia

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