Call for Paper - March 2023 Edition
IJCA solicits original research papers for the March 2023 Edition. Last date of manuscript submission is February 20, 2023. Read More

A New Approach to Image Denoising based on Wiener-LMMSE Scheme

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 45 - Number 22
Year of Publication: 2012
Abhinandan Kalita
Md. Sajjad Hossain
Kandarpa Kumar Sarma

Abhinandan Kalita, Md. Sajjad Hossain and Kandarpa Kumar Sarma. Article: A New Approach to Image Denoising based on Wiener-LMMSE Scheme. International Journal of Computer Applications 45(22):41-47, May 2012. Full text available. BibTeX

	author = {Abhinandan Kalita and Md. Sajjad Hossain and Kandarpa Kumar Sarma},
	title = {Article: A New Approach to Image Denoising based on Wiener-LMMSE Scheme},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {45},
	number = {22},
	pages = {41-47},
	month = {May},
	note = {Full text available}


Several noise removal techniques have proven their worth in image processing applications. After an overview of some image denoising approaches, we introduce a LMMSE-based denoising technique with wavelet multiscale model and wiener filter in spatial domain. This proposed denoising technique stands out prominent in terms of SNR, MSE and PSNR compared to some more denoising techniques (also proposed in this paper). The Overcomplete Wavelet Expansion (OWE) which is also employed, provides better result compared to Orthogonal Wavelet Transform (OWT). Moreover, some fine details of the image such as edges, curves etc. is preserved using the LMMSE rule.


  • R. E. Woods, R. C. Gonzalez, "Digital Image Processing", Pearson Prentice Hall, 3rd ed. , 2009.
  • G. Y. Chen, T. D. Bui and A. Krzyzak, "Image denoising using neighbouring wavelet coefficients", IEEE, pp. II (917-920), ICASSP, 2004.
  • Michel Misiti, Yves Misiti, Georges Oppenheim, Jean-Michel Poggi, "Wavelet Toolbox for use with MATLAB", User's guide, version 2. 1, pp. 1-37.
  • Aleksandra Pizurica, Wilfried Philips, Ignace Lemahieu and Marc Acheroy, "A Versatile Wavelet Domain Noise Filtration Technique for Medical Imaging", IEEE transactions on medical imaging, vol. 22, no. 3, pp. 323-331, March 2003.
  • S. Sudha, G. R. Suresh, R. Sukanesh, "Wavelet Based Image Denoising using Adaptive Thresholding", International Conference on Computational Intelligence and Multimedia Applications, pp. 296-300, 2007.
  • N. G. Resmi, K. P. Soman, K. I. Ramachandran, "Insight into wavelets from theory to practice", PHI, 3rd ed. , New Delhi, 2011
  • S. Grace Chang, Bin Yu and Martin Vetterli, "Adaptive Wavelet Thresholding for Image Denoising and Compression", IEEE transactions on image processing, vol. 9, no. 9, pp. 1532-1546, September 2000.
  • S. Kumar, P. Kumar, M. Gupta, A. K. Nagawat "Performance Comparison of Median and Wiener Filter in Image De-noising" International Journal of Computer Applications (0975 – 8887) Volume 12– No. 4, November 2010.
  • D. L. Donoho and I. M. Johnstone, "Ideal spatial adaptation by wavelet shrinkage,"? Biometrika, vol. 81, no. 3, pp. 425–455, 1994.
  • D. L. Donoho, I. M. Johnstone, G. Kerkyacharian, and D. Picard, "Wavelet shrinkage: Asymptopia?", J. Roy. Statist. Assoc. B, vol. 57, no. 2, pp. 301–369, 1995.
  • S. G. Chang, B. Yu, and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression," IEEE Trans. Image Process. , vol. 9, no. 9, pp. 1532–1546, Sep. 2000.
  • J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli,"Image denoising using scale mixtures of Gaussians in the wavelet domain", IEEE Trans. Image Process. , vol. 12, no. 11, pp. 1338–1351, Nov. 2003.
  • Pizurica and W. Philips, Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising" IEEE Trans. Image Process. , vol. 15, no. 3, pp. 654–665, Mar. 2006.
  • L. Sendur and I. W. Selesnick, "Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency,"IEEE Trans. Signal Process. vol. 50, no. 11, pp. 2744–2756, Nov. 2002.
  • L. Sendur and I. W. "Selesnick, Bivariate shrinkage with local variance estimation,"IEEE Signal Process. Lett. , vol. 9, no. 12, pp. 438–441, Dec. 2002.
  • F. Luisier, T. Blu, and M. Unser,"A new sure approach to image denoising: Inter-scale orthonormal wavelet thresholding," IEEE Trans. Image Process. , vol. 16, no. 3, pp. 593–606, Mar. 2007.
  • D. L. Donoho,"Denoising and soft thresholding," IEEE. Transactions. Information. Theory, Vol. 41, PP. 613-627, 1995.
  • D. L. Donpho, and I. M. Johnstone, "Adaptive to unknown smoothness via wavelet shrinkage", Journal of American statistical ASSOC. , VOL. 90, NO. 90, PP. 1200-1224, 1995.
  • D. L. Donpho, and I. M. Johnstone, "Ideal spatial adaptation via wavelet shrinkage", Biometrika, VOL. 81, PP. 425-455,1994.
  • I. Pitas and A. N. Venetsanopoulos, "Nonlinear Digital Filters: Principles and applications", Boston, MA: Kluwer. 1990.
  • J. Liu and P. Moulin, ?Information-theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients, IEEE Trans. Image Process. , vol. 10, no. 11, pp. 1647–1658, Nov. 2001.
  • Lei Zhang, Paul Bao,, ?Multiscale LMMSE-Based Image Denoising With Optimal Wavelet Selection, IEEE Transactions On Circuits And Systems , vol. 15, NO. 4, Apr, 2005.
  • M. K. Mihçak, I. Kozintsev, K. Ramchandran, and P. Moulin, "Low complexity image denoising based on statistical modeling of wavelet coefficients," IEEE Signal Process. Lett. , vol. 6, no. 12, pp. 300–303, Dec. 1999.
  • X. Li and M. Orchard, "Spatially adaptive image denoising under overcomplete expansion," in Int. Conf. Image Process. , Vancouver, Canada, pp. 300–303, Sep. 2000.