Image Denoising in Wavelet Domain with Filtering and Thresholding

  • Authors

    • K Sumathi
    • Ch Hima Bindu
    2018-09-01
    https://doi.org/10.14419/ijet.v7i3.34.19218
  • Enhancement, Discrete wavelet TransformationDenoisingfilters, Threshold.Virtual reality.

  • Abstract

    In this paper, the proposed method is implemented for removal of salt & pepper and Gaussian noise of black & white & color images to
    acquire the quality output. In this work initially wavelet coefficients are extracted for noisy images. Later apply denoise filtering
    technique on the high transform sub bands of noisy images (either color/ B & W) using new laplacian filters with 4 directions. Finally
    threshold of an image is generated to extract denoisy coefficients. At last inverse of above subband coefficients can give denoise image
    for further processing. The proposed method is verified against various B & W/color images and it gives a better PSNR (Peak Signal to
    Noise Ratio) & MI (Mutual Information). These values are compared with different noise densities and analyzed visually.

  • References

    1. Ch.Hima Bindu, K.Sumathi, “Denoising of Images With Filtering
      and Thresholding †International conference on research in
      Engineering Computers & Technology, PP:142-146, ISBN:5978-
      81-908388-7-0, sep, 8-10, 2016, Thiruchy, Tamilnadu, INDIA.
      [2] Jyotsna Pati, et al., “A Comparative Study of Image Denoising
      Techniquesâ€, IJIR S, Engineering and Technology, Vol. 2, Issue 3,
      March 2013, ISSN: 2319-875.
      [3] Noble, et al., “A Comprehensive Review of Image Enhancement
      Techniquesâ€, journal of computing, volume 2, issue 3, march 2010,
      issn 2151-9617Gonzanez.c,Richard..E,“Digital Imahe Processingâ€,
      3rd edition,pearson.
      [4] K.Jain, “Fundamentals of digital image processingâ€. Prentice-
      Hall, 1989.
      [5] Mukesh C.et al., “Survey of Image Denoising Techniquesâ€.
      [6] R. Yang, et al., “Optimal weighted median filters under structural
      constraints,†IEEE Trans. Signal Processing, vol. 43, pp. 591–604,
      Mar 1995.
      [7] Ben Hamza, et al., “Removing noise and preserving details with
      relaxed median filters,†J. Math. Imag. Vision, vol. 11, no. 2, pp.
      161–177, Oct. 1999.
      [8] A.K.Jain,Fundamentals of digital image processing. Prentice-
      Hall,1989.
      [9] David L.Donoho et al., “Ideal spatial adaption via wavelet
      shrinkageâ€, Biometrika, vol.81, pp 425-455, September 1994.
      [10] David L. Donoho et al.,“Adapting to unknown smoothness via
      wavelet shrinkageâ€, JASA, vol.90, no432, pp.1200-1224,
      December1995. National Laboratory, July 27, 2001.
      [11] S.Gopi krishna, et al., “ Removal of High Density Salt and Pepper
      Noise Through Modified Decision Based Unsymmetric Trimmed
      Median Filter†ISSN: 2248-9622, Vol. 2, Issue 1, Jan-Feb 2012,
      pp.090-094.
      [12] J. Romberg, et al.,"Bayesian wavelet domain image modeling
      using hidden Markov models," IEEE Transactions on
      ImageProcessing, vol. 10, pp. 1056-1068, July 2001.
      [13] Ch.Hima Bindu et al., “Performance Analysis of Multi Source
      Fused Medical Images Using Multiresolution transformsâ€,
      (IJACSA), Vol. 3, No. 10, 2012.

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  • How to Cite

    Sumathi, K., & Hima Bindu, C. (2018). Image Denoising in Wavelet Domain with Filtering and Thresholding. International Journal of Engineering & Technology, 7(3.34), 327-330. https://doi.org/10.14419/ijet.v7i3.34.19218

    Received date: 2018-09-07

    Accepted date: 2018-09-07

    Published date: 2018-09-01