We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Improved Image Denoising Algorithm using Dual Tree Complex Wavelet Transform

by B. Chinnarao, M. Madhavilatha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 20
Year of Publication: 2012
Authors: B. Chinnarao, M. Madhavilatha
10.5120/6376-8788

B. Chinnarao, M. Madhavilatha . Improved Image Denoising Algorithm using Dual Tree Complex Wavelet Transform. International Journal of Computer Applications. 44, 20 ( April 2012), 1-6. DOI=10.5120/6376-8788

@article{ 10.5120/6376-8788,
author = { B. Chinnarao, M. Madhavilatha },
title = { Improved Image Denoising Algorithm using Dual Tree Complex Wavelet Transform },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 20 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number20/6376-8788/ },
doi = { 10.5120/6376-8788 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:36:02.592944+05:30
%A B. Chinnarao
%A M. Madhavilatha
%T Improved Image Denoising Algorithm using Dual Tree Complex Wavelet Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 20
%P 1-6
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image denoising methods are used to remove the noise components without affecting the important image features and content. Wavelet transforms represents image energy in compact way and this representation helps to find threshold between noisy feature and important image features. In this work we proposed a contextual information based thresholding method in Dual tree complex wavelet transform. We compared our method with other two denoising methods. For comparison purpose we used two standard image processing images using different Gaussian noise variance.

References
  1. Donoho. D. L,Johnstone. I. M, "Ideal spatial adaptation via wavelet shrinkage", Biometrika,81,pp. 425-455,1994.
  2. Aglika Gyaourova Undecimated wavelet transforms for image denoising, November 19, 2002.
  3. C Sidney Burrus, Ramesh A Gopinath, and Haitao Guo, "Introduction to wavelet and wavelet transforms", Prentice Hall1997.
  4. S. Mallat, A Wavelet Tour of Signal Processing, Academic, New York, second edition, 1999.
  5. R. C. Gonzalez and R. Elwood?s, Digital Image Processing. Reading, MA: Addison-Wesley, 1993.
  6. L. Sendur and I. W. Selesnick, "Bivariate shrinkage with local variance estimation," IEEE Signal Proc. Letters, vol. 9, pp. 438- 441, Dec. 2002.
  7. N. G. Kingsbury," The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters", In the Proceedings of the 8th IEEE Digital Signal Processing Workshop, Bryce Canyon Aug 1998.
  8. J. Neumann and G. Steidl, "Dual–tree complex wavelet transform in the frequency domain and an application to signal classification", International Journal of Wavelets, Multiresolution and Information Processing IJWMIP, 2004.
  9. Javier Portilla, Vasily Strela, Martin J. Wainwright, Eero P. Simoncelli, Adaptive Wiener Denoising using a Gaussian Scale Mixture Model in the wavelet Domain, Proceedings of the 8th International Conference of Image Processing Thessaloniki, Greece. October 2001.
  10. Zhou Dengwen, Cheng Wengang, "Image denoising with an optimal threshold and neighbouring window," Pattern Recognition Letters, vol. 29, no. 11, pp. 1694–1697, 2008
  11. Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, Eero P. Simoncelli,'' Image Quality Assessment: From Error Visibility to Structural Similarity
  12. Zhou Dengwen ,An Image Denoising Algorithm with an adaptive window IEEE 2007
Index Terms

Computer Science
Information Sciences

Keywords

Neighshrinksure Dual Tree Complex Wavelet Thresholding