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Performance of Wavelet, Contourlet and 2D PCA Transforms for Image Denoising
Abstract
This paper uses the wavelet, Contourlet transform and
the two dimensional Principle Component Analysis (2DPCA) for
image denoising. The simulation results is carried out to demonstrate
the performance of proposed techniques and the result shows that
2DPCA is superior over contourlet and wavelet transform in
maintaining high peak signal –to-noise –ratio. The denoising
algorithm is validated by numerical experiments on different standard
images and roughness images. Numerical result shows that the
proposed method can obtain higher PSNR and less visual artifacts
compared with other methods. Also, Contourlet can provide a much
more detailed representation for natural images with abundant
textural information than Wavelets. The improved two dimensional
PCA (2DPCA) extended the traditional principal component analysis
theory to the two dimensional situation, and proved to be especially
efficient in the analysis of images.
Keywords
References
E.J. Candèsand D.L. Donoho.“Curvelets, multiresolution representation,
and scaling laws,” Wavelet Applications in Signal and Image Processing
VIII, A. Aldroubi, A. F. Laine, M. A. Unser eds., Proc. SPIE 4119,
L. Kaur, S. Gupta and R.C. Chauhan, Image Denoising using Wavelet
Thresholding, ICVGIP 2002.
S.Jean-Luc,E.J. Candes, and D. L. Donoho, "The curvelet transform for
image denoising," Image Processing, IEEE Transactions on, vol. 11, pp.
-684, 2002.
R. Eslami and H. Radha, “On Low Bit-Rate Coding Using the
Contourlet Transform,” Conference Record of the Thirty-Seventh
Asilomar Conference on Signals, Systems and Computers, vol.2, pp.
-1528, 2003.
R. Eslami and H. Radha, “The Contourlet transform for Image
Denoising Using Cycle Spinning,” Conference Record of the Thirty-
Seventh Asilomar Conference on Signals, Systems and Computers,
vol.2, pp.1982-1986,2003
J. Yang and D. Zhang, “Two-dimensional PCA: A new approach to
appearance-based face representation and recognition,” Pattern Analysis
and Machine Intelligence, IEEE Transactions on, vol. 26, pp. 131-137,
Abdullah Al Muhit, Md. Shabiul Islam and Masuri Othman,“VLSI
Implementation of Discrete Wavelet Transform (DWT) for Image
Compression” 2nd International Conference on Autonomous
Robots and Agents December 13-15, 2004 Palmerston North, New
Zealand.
M. N. Do and M. Vetterli, "The contourlet transform: an efficient
directional multiresolution image representation," Image Processing,
IEEE Transactions on, vol. 14, pp. 2091-2106, 2005.
Fengxiang Qiao, “ Introduction to Wavelet, A Tutorial”, workshop 118
on Wavelet Application in Transportation Engineering, January
,2005.
Q. Miao and B. Wang, “A Novel Image Fusion Method Using
Contourlet Transform,” IEEE Proceedings of the 2006 International
Conference on Communications, Circuits and Systems, vol. 1, pp. 548-
, 2006.
C. Gonzalez-Garcia, J. H. Sossa-Azuela, E. M. Felipe-Riveron and O.
Pogrebnyak,” Image Retrieval Based on Wavelet Transform and Neural
Network Classification”Computación ySistemas Vol. 11 No.2, 2007, pp
-156.
Bo Zhang, Jalal M. Fadili, and Jean-Luc Starck, “Wavelets, Ridgelets,
and Curvelets for Poisson Noise Removel,” IEEE Transactions on Image
Processing, vol. 17, no. 5, pp. 1093-1108, 2008.
Anil A. Patil, Jyoti Singhai, “Image denoising using curvelet transform:
an approach for edge preservation,” Jounal of Scientific & Industrial
Research, vol. 69, pp. 34-38, 2010
Zhe Liu, Huanan Xu,” Image Denoising using Contourlet and Two-
Dimensional Principle Component Analysis” IEEE Proceedings of
the 2010.
A. K. Jain ,“Fundamentals of Digital Image Processing”, PHI New
Jersy.
V.M.Gadre, IIT,Mumabai,“Wavelets-A Tutorial”.
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