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Frontiers in Signal Processing
FSP > Volume 1, Number 1, July 2017

A Hybrid Image Denoising Technique Using Neighbouring Wavelet Coefficients

Download PDF  (332.1 KB)PP. 41-48,  Pub. Date:July 13, 2017
DOI: 10.22606/fsp.2017.11005

Author(s)
Mantosh Biswas, Hari Om
Affiliation(s)
Department of Computer Engineering, NIT Kurukshetra, India; Department of Computer Science & Engineering, IIT Dhanbad, India
Abstract
This paper proposes a hybrid image denoising technique using neighbouring wavelet coefficients. The NeighShrink method groups the wavelet coefficients in non overlapping blocks and then thresholds empirically them blockwise. This method does not give good quality of image since it removes too many small wavelet coefficients. Our proposed scheme retains the modified coefficients and also gives good performance in terms of peak signal-to-noise ratio.
Keywords
Wavelet thresholding, image denoising, neighbouring coefficients, peak signal to-noise.
References
  • [1]  C. S. Burrus, R. A. Gopinath and H. Guo, “Introduction to wavelet and wavelet transforms,” Prentice Hall, 1997.
  • [2]  M. Jansen,“Noise Reduction by Wavelet Thresholding,” Springer – Verlag New York Inc., 2001.
  • [3]  Antoniadis and J. Bigot, “Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study,” Journal of Statistical Software, Vol. 6, Issue 6, pp. 1–83, 2001.
  • [4]  D. L. Donoho and I. M. Johnstone, “Wavelet shrinkage: Asymptotic?” J.R. Stat. Soc. B, Vol. 57, No. 2, pp. 301-369, 1995.
  • [5]  D. L. Donoho, “De-Noising by Soft Thresholding,” IEEE Trans. Info., Vol. 41, No. 3, pp. 613–627, 1995.
  • [6]  D. L. Donoho and I. M. Johnstone, “Adapting to Unknown Smoothness via Wavelet Shrinkage,” Journal of American Statistical Association, Vol. 90, No. 432, pp. 1200-1224, 1995.
  • [7]  D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation via wavelet shrinkage,” Biometrika, Vol. 81, No. 3, pp. 425-455, 1994.
  • [8]  X.P. Zhang and M. D. Desai, “Adaptive denoising based on SURE risk,” IEEE Signal Process. Lett., Vol. 5, No.10, pp. 265–267, 1998.
  • [9]  S. G. Chang, B. Yu and M. Vetterli, “Adaptive Wavelet Thresholding for Image Denoising and Compression,” IEEE Trans. Image Processing, Vol. 9, No. 9, pp. 1532-1546, 2000.
  • [10]  Elyasi and S. Zarmehi, “Elimination Noise by Adaptive Wavelet Threshold,” World Academy of Science, Engineering and Technology, pp. 462-466, 2009.
  • [11]  T. T. Cai and B.W. Silverman, “Incorporating information on neighboring coefficients into wavelet estimation,” Sankhya, Ser. B, Vol. 63, No. 2, pp. 127-148, 2001.
  • [12]  G. Y. Chen, T. D. Bui and A. Krzy˙zak, “Image denoising with neighbor dependency and customized wavelet and threshold,” Pattern Recognition, Vol. 38, No. 1, pp. 115–124, 2005.
  • [13]  G.Y. Chen, T. D. Bui and A. Krzyzak, “Image denoising using neighbouring wavelet coefficients,” ICASSP, pp.917-920, 2004.
  • [14]  S. K. Mohideen, S. A. Perumal and M. M. Sathik, “Image De-noising using Discrete Wavelet transform,”IJCSNS International Journal of Computer Science and Network Security, Vol. 8, No. 1, pp. 213-216, 2008.
  • [15]  P. Kittisuwan and W. Asdornwised, “Wavelet-Based Image Denoising using NeighShrink and BiShrink Threshold Functions,” ECTI-CON, pp. 497-500, 2008.
  • [16]  Daubechies, “Ten Lectures on Wavelets,” Proc. CBMS-NSF Regional Conference Series in Applied Mathematics. Philadelphia, PA: SIAM, Vol. 61, 1992.
  • [17]  B. C. Rao1 and M. M. Latha, “Selective neighbouring wavelet coefficients approach for image denoising,”International Journal of Computer Science and Communication, Vol. 2, No. 1, pp. 73–77, 2011.
  • [18]  G. Chen and W. Zhu, “Image Denoising Using Three Scales of Wavelet Coefficients,” Advances in Neural Networks, Vol. 5264, pp. 376-383, 2008.
  • [19]  S. Gupta and L. Kaur, “Wavelet Based Image Compression using Daubechies Filters,” In proc. 8th National conference on communications, I.I.T. Bombay, NCC, 2002.
  • [20]  Y. Yang and Y. Wei, “Neighboring Coefficients Preservation for Signal Denoising,” Circuits, Systems, and Signal Processing, Vol. 31, No. 2, pp. 827-832, 2012.
  • [21]  Divya Guglani and Nitin Kumar Katyal, “Noise Removal using Double Density Complex Dual Tree Transform with NeighShrink SURE and Median Filter,” 1st International Conference on Next Generation Computing Technologies, pp. 1-4, 2015.
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