DOI QR코드

DOI QR Code

Image Denoising using Adaptive Threshold Method in Wavelet Domain

  • Gao, Yinyu (Department of Control & Instrumentation Eng., Pukyong National University) ;
  • Kim, Nam-Ho (Department of Control & Instrumentation Eng., Pukyong National University)
  • Received : 2011.11.04
  • Accepted : 2011.11.30
  • Published : 2011.12.31

Abstract

Image denoising is a lively research field. Today the researches are focus on the wavelet domain especially using wavelet threshold method. We proposed an adaptive threshold method which considering the characteristic of different sub-band, the method is adaptive to each sub-band. Experiment results show that the proposed method extracts white Gaussian noise from original signals in each step scale and eliminates the noise effectively. In addition, the method also preserves the detail information of the original image, obtaining superior quality image with higher peak signal to noise ratio(PSNR).

Keywords

References

  1. Mehdi Nasri and Hossein Nezamabadi-pour, "Image denoising in the wavelet domain using a new adaptive thresholding function", January of Neuro Computing, vol. 72, pp. 1012-1025, 2009.
  2. J. Portilla, V. Strela, M. Wainwright and E. Simoncelli, "Image denoising using scale mixture of Gaussians in the wavelet domain", IEEE Trans. Image Process., vol.12, pp. 1338-1350, 2003. https://doi.org/10.1109/TIP.2003.818640
  3. Florian Luiser and Thierry Blu, "A new sure approach to image denoising: interscale orthonormal wavelet thresholding", IEEE Transactions on Image Processing, vol. 16, Mo. 3, pp. 593-606, 2007. https://doi.org/10.1109/TIP.2007.891064
  4. Gonzalez R. C and Woods R. E, "Digital Image Processing", Addison-Wesley, 2003.
  5. Donoho, D. L. and Johnstone, "Ideal Spatial Adaptation via Wavelet Shrinkage", Technical Report, Department of Statistics, Stanford University, Tentatively, 1992.
  6. D. L. Donoho and I. M. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage," J. Amer. Statist. Assoc., vol. 90, no. 432, pp. 1200-1224, Dec. 1995. https://doi.org/10.1080/01621459.1995.10476626
  7. D.L. Donoho, "De-Noising by Soft Thresholding", IEEE Trans. Info. Theory 43, pp. 933-936, 1993.
  8. Gao Yinyu and Nam-Ho Kim, "Restoration of Images Contaminated by Mixed Gaussian and Impulse Noise using a Complex Method", International Journal of KIMICS, vol. 9, No. 3, pp. 336-340, June 2011.
  9. Gao Yinyu and Nam-Ho Kim, "A Study on Image Restoration Algorithm in Random-valued Impulse Noise Environment", International Journal of KIMICS, vol. 9, No. 3, pp. 331-335, June 2011.
  10. Wei Zhang, Fei Yu and Hong-mi Guo, "Improved adaptive wavelet threshold for image denoising", Control and Decision Conference, pp. 5958-5963, 2009.
  11. J. E. Fowler, "The Redundant Discrete Wavelet Transform and Additive Noise", IEEE Signal Processing Letters, vol. 12, pp. 629-632, Sept. 2005. https://doi.org/10.1109/LSP.2005.853048
  12. Q. Pan, L. Zhang, G. Dai and H. Zhang, "Two Denoising Methods by Wavelet Transform", IEEE Transactions on Signal Processing, vol. 47, pp. 3401-3406. Dec. 1999. https://doi.org/10.1109/78.806084
  13. M. K. Mihcak, I. Kozintsev, K. Ramchandran and P. Moulin, "Low-complexity image denoising based on statistical modeling of wavelet coefficients, IEEE Signal Process. Lett, pp. 6300-303, 1999. https://doi.org/10.1109/97.803428
  14. J. Portilla, V. Strela, M. Wainwright and E. Simoncelli, "Image denoising using scale mixture of Gaussians in the wavelet domain", IEEE Trans, Image Process, vol. 12, pp. 1338-1350, 2003. https://doi.org/10.1109/TIP.2003.818640
  15. H. Rabbani and M. Vafadoost, "Wavelet based image denoising based on amixture of Laplace distributions", Iran. J. Sci. Technol. Trans. BEng, pp.711-733, 2003.
  16. Selesnick I W, Baraniuk R G and Kingsbury N G, "The Dual-Tree Complex Wavelet Transform. IEEE Signal Processing Magazine, vol. 22, No. 6, pp. 123-151, 2005. https://doi.org/10.1109/MSP.2005.1550194
  17. Choi H, Romberg J K and Baraniuk R G, "Markov tree modeling of complex wavelet transforms", Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP2000, vol.1, pp. 133-136, 2000.
  18. Ye Z and Lu C C, "A complex wavelet domain Markov model for image denoising", Proceedings of the IEEE International Conference on Image Processing, ICIP2003, vol.3, pp. 365-368, 2003.
  19. Tyagarajan K, "Image compression in the wavelet domain", Image and video compression, pp. 259-300, 2011.
  20. Wen-hua Zhang and Ya-song Chen, "Image scrambling techonology by wavelet transformation and prime theory", IEEE Conference on Control, Automation and Systems Engineering(CASE), pp. 1-3, 2011.

Cited by

  1. A Study on Improved Denoising Algorithm for Edge Preservation in AWGN Environments vol.16, pp.8, 2012, https://doi.org/10.6109/jkiice.2012.16.8.1773
  2. A Study of Electrical and Optical Method of Safety Standards for diagnosis of Power Facility using UV-IR Camera vol.27, pp.4, 2013, https://doi.org/10.5207/JIEIE.2013.27.4.054