Open Access Open Access  Restricted Access Subscription or Fee Access

Analysis of Image Restoration Techniques using Fourier and Wavelet Transform

Poonam Galhotra, Neeraj Gill, R. K. Bansal

Abstract


The task of image restoration is to restore the image removing all of the degradations present in it. This degradation may be due to the motion or due to presence of noise. This noise can be of any type such as Gaussian, salt and pepper, speckle or poisson. We have mainly considered salt and pepper noise. In this paper, standardized techniques have been used for image restoration which involves techniques using fourier transform and wavelet transform. Also, quantitative evaluation of various image restoration methods has been done based on parameters such as contrast, correlation, energy and homogeneity.


Keywords


Fourier, Inverse, Wavelet, Weiner.

Full Text:

PDF

References


A.K. Ng, T.S. Koh, and C.H. Thng “A Level-Wavelet- Dependent Scheme for Image Denoising Via Undecimated Wavelet Transform” Signal and Image Processing - 2007, pp. 576-586.

Cláudio R. Jung Jacob Scharcanski “Wavelet Transform Approach To Adaptive Image Denoising And Enhancement” Journal of Electronic Imaging, April 2004, Volume 13, Issue 2, pp. 278-285

G.Y. Chen, T.D. Bui,A. Krzyzak “Image denoising withneigh bour dependency and customized wavelet and threshold” Pattern Recognition 38, 2005, pp. 115 – 124

S. G. Chang, B. Yu and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising”, . IEEE Trans.on Image Processing, Vol. 9, No. 9, pp. 1522-1531, 2000. .

5.Jung C.R., Scharcanski . J. “Adaptive Image Denoising In Scale-Space Using The Wavelet Transform ” 2001 pp. 1530-1834

Hui Cheng, Qiuze Yu, Jinwen Tian, Jian Liu “Image Denoising Using Wavelet and Support Vector Regression” Proceedings of the Third International Conference on Image and Graphics (ICIG’04)

J. D. Villasenor, B. Belzer and J. Liao, „Wavelet Filter Evaluation for Image Compression”, IEEE Trans. Image Proc., August 1995.

J. S. Lee, “Digital image enhancement and noise filtering by use of local statistics”, IEEE PAMI, vol. 2, no. 2, pp. 165 168, 1980.

S. G. Chang, B. Yu and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising”, Proc. IEEE Int.Conf. on Image Processing, Oct. 1998.

S. G. Chang, B. Yu and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising”, . IEEE Trans.on Image Processing, Vol. 9, No. 9, pp. 1522-1531, 2000.

X. Li and M. T. Orchard: “Spatially Adaptive Image Denoising under Overcomplete Expansions”, Proc. IEEE Int. Conf. on Image Processing,Vancouver, 2000.

D. L. Donoho and I. M. Johnstone, “Ideal Spatial Adaptation Via Wavelet Shrinkage ”, Biometrica, vol.81, pp.425 –455,1994.

M. K. Mihcak, I. Kozintsev, and K. Ramchandran, “Spatially Adaptive Statistical Modeling of Wavelet Image Coefficients and Its Application to Denoising,” Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, vol. 6, Mar. 1999, pp. 3253–3256.

D. Marpe, H. L. Cycon, G. Zander, K. U. Barthel, “Context-based Denoising of Images Using Iterative Wavelet Thresholding” Proc. SPIE,Vol. 4671, pp. 907-914, Jan. 2002.

Tinku Acharya, Ajoy.K.Ray, “IMAGE PROCESSING –Principles and Applications”, Hoboken, New Jersey, A JOHN WILEY & SONS, MC.Publication, 2005.

Detail information about the Multiresolution Analysis and The Continuous Wavelet Transform6, http://users.rowan.edu/~polikar/WAVELETS/WTpart3.html

Detail information about the Fundamental Concepts of Transformations http://users.rowan.edu/~polikar/WAVELETS/WTpart1.html

Detail information about Wavelet Analysis http://www.mathworks.com/access/helpdesk/help/toolbox/wavelet

Aglika Gyaourova, Chandrika Kamath and Imola K. Fodor,“Undecimated wavelet transforms for image de-noising”, November 19,2002

S.Kother Mohideen, Dr. S. Arumuga Perumal, Dr. M.Mohamed Sathik,“Image de-noising using discrete wavelet transform”, International Journal of Computer Science and Network Security, vol.8 no.1, January 2008

Detail information about Multiresolution Analysis: The Discrete Wavelet Transform8, http://users.rowan.edu/~polikar/WAVELETS/WTpart4.html

De Creane Lars, Bats Andy, Schaeps Tim, “Medical image processing:denoising”, Vision Lab, Hogeschool Antwerpen, June 2006

Rafael C.Gonzalez, Richard E.Woods and Steven L.Eddins, “Digital Image Processing Using MATLAB”, Pearson Education (Singapore) Pte Ltd., Indian Branch, 482 F.I.E. Patparganj, Delhi 110092, India, 2004

Standard original images, http://www.ece.rice.edu/~wakin/images/

L. Debnath. Wavelets and Signal Processing. Birkhauser Boston, Boston,U.S.A., 2003.

O. Rioul and M. Vetterli. Wavelets and Signal Processing. IEEE SP Magazine, pages 14–38, October 1991.

G. Strang. Wavelets and Dilation Equations: A brief introduction .SIAM Review, 31(4):614–627, Dec. 1989.

G. Strang and T. Nguyen. Wavelets and Filter Banks Wellesley-Cambridge Press, 1996.

Saeed V. Vaseghi. Advanced Digital Signal Processing and Noise Reduction. Wiley & Teubner, West Sussex, U.K., second edition, 2000.

Z. Wang, Y. Yu, and D. Zhang. Best Neighborhood Matching: An Information Loss Restoration Technique for Block-Based Image Coding Systems. IEEE Transactions on Image Processing, 7(7):1056–1061, July 1998


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.