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Performance Analysis of InterpolatedShrink method in Image De-Noising

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International Journal of Computer Applications
© 2011 by IJCA Journal
Number 8 - Article 1
Year of Publication: 2011
Authors:
J S Bhat
B N Jagadale
10.5120/1972-2643

J S Bhat and B N Jagadale. Article: Performance Analysis of InterpolatedShrink method in Image De-Noising. International Journal of Computer Applications 15(8):1–6, February 2011. Full text available. BibTeX

@article{key:article,
	author = {J S Bhat and B N Jagadale},
	title = {Article: Performance Analysis of InterpolatedShrink method in Image De-Noising},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {15},
	number = {8},
	pages = {1--6},
	month = {February},
	note = {Full text available}
}

Abstract

The de-noising of an image corrupted by Gaussian noise is a classical problem in signal or image processing. An image is often corrupted by noise during its acquisition and transmission. Image de-noising is used to reduce the noise while retaining the important features in the image. Always there exists a tradeoff between the removed noise and the blurring in the image. The use of wavelet transform for signal de-noising has emerged as an important technique during the last decade. The wavelet transform is preferred over conventional Fast Fourier Transform(FFT) based image de-noising technique ,because of its capability to give detailed spatial-frequency information. In this paper, we tried to analyze the performance of InterpolatedShrink method in image de-noising using various wavelet family, such as Haar,Doubechies,Symlet and Coiflets, for Gaussian noise.

Reference

  • A. K. Jain, 1989. Fundamentals of digital image processing. Prentice-Hall Inc., Englewood Cliffs, New Jersey.
  • J. S. Lee, 1980. Digital image enhancement and noise filtering by use of local statistics. IEEE trans. On PAMI, Vol.2, pp. 165-168.
  • A Bruce and H. Gao, Applied, 1996. Wavelet Analysis with S-PLUS. Springer Verlag.
  • Motawani M. C., Gadiya M. C., Motwani R. C. and Harris Jr. F. C. 2004. Survey of Image Denoising Techniques. proceedings of GSPx, Santa Clara, CA.
  • R. T. Ogden, 1997. Essential Wavelets for Statistical Applications and data Analysis. Bikhauser.
  • B.Vidakovic, 1999. Staistical Modeling by Wavelets, Wiley Series in Probability and Statistics. Jhon Wily & Sons, Inc.
  • Y. Oppenheim, J. M. Poggi, M. Misiti, Y. Misiti, 2001. Wavelet Toolbox. The MathWorks, Inc., Natick, Massachusetts. (April 2001).01760
  • D. Donoho, 1993. De-Noising by SofttThresholding. IEEE Trans. Info. Theory, vol.43, pp.933-936.
  • D. Donoho and I. Jonstne, 1995. Adapting to unknown Smoothness via wavelet shrinkage. Journal of the American Statistical Association, vol. 90, (December 1995) pp.1200-1244,
  • S. Mallat, 1998. A Wavelet tour of Signal processing. Academic Press, San Diego, USA.
  • Stein, C. 1981. Estimation of the mean of a multivaarte normal distribution. Ann. Statist, vol.9, 1135-1151.
  • S. G. Chang, B. Yu, and M. Vetterli, 2000. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Processing, vol. 9, pp.1532-1546.
  • Lakhavinder kaur, Savita Gupta and R. C. Ghauhan, 2002. Image Denoising using Wavelet Thresholding. Third conference on Computer Vision, Graphics and Image Processing, (Dec 16-18 2002). India
  • T .T. Cai and B. W. Silverman, 2001 Incorporating information on neighboring coefficients into wavelet estimation. Sankhya A , vol. 63, pp. 127-148.
  • M. K. Mihcak, K. R. I Kozintsev and P. Moulin, 2006.Low – Complexity image denoising based on statistical Modeling wavelet coefficients. IEEE Signal Trans. Image Proce,. Vol15, pp.654-665.
  • L. Sendur and I. W. Selesnick, 2002. Bivariate shrinkage with local varience estimation. IEEE Signal Process. Lett. Vol. 9, pp. 438-441.
  • Chen G. Y., Bui T. D., Krzyzak A. 2005. Image denoising with neighbor dependency and customized wavelet and threshold. Pattern Recognition, Vol. 38, pp.115-124.
  • J. S. Bhat, B.N. Jagadale, Lakshminarayan H. K. 2010. Image De-noising with an Optimal threshold by Decimated Wavelet transorm. IJRTET (Nov 2010) Vol.4, No.2.