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Performance Analysis of Image de-noising using Fuzzy and Wiener Filter in Wavelet Domain

by Mayank Mehra, Devendra Moraniya, Dhiiraj Nitnawwre
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 101 - Number 3
Year of Publication: 2014
Authors: Mayank Mehra, Devendra Moraniya, Dhiiraj Nitnawwre
10.5120/17669-8496

Mayank Mehra, Devendra Moraniya, Dhiiraj Nitnawwre . Performance Analysis of Image de-noising using Fuzzy and Wiener Filter in Wavelet Domain. International Journal of Computer Applications. 101, 3 ( September 2014), 34-37. DOI=10.5120/17669-8496

@article{ 10.5120/17669-8496,
author = { Mayank Mehra, Devendra Moraniya, Dhiiraj Nitnawwre },
title = { Performance Analysis of Image de-noising using Fuzzy and Wiener Filter in Wavelet Domain },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 101 },
number = { 3 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 34-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume101/number3/17669-8496/ },
doi = { 10.5120/17669-8496 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:30:45.256524+05:30
%A Mayank Mehra
%A Devendra Moraniya
%A Dhiiraj Nitnawwre
%T Performance Analysis of Image de-noising using Fuzzy and Wiener Filter in Wavelet Domain
%J International Journal of Computer Applications
%@ 0975-8887
%V 101
%N 3
%P 34-37
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Images are nowadays, very fundamental type data for transmission. Due to the various components and high speed transmission, images are corrupted by the noises. The Image denoising is required at the receiver end for the faithful communication. There are several methods for image denoising in spatial and transform domain. The current trends of the image denoising research are the evolution of mixed domain methods. In this paper, a mixed domain image denoising method is proposed, which is based on the wavelet transform, median filter and nonlinear diffusion based methods. The wavelet transform is used in this paper to convert the spatial domain image to wavelet domain coefficients. WT produces approximation, horizontal detail, vertical detail and diagonal detail coefficient which represent the various spatial frequency bands. The detail component are removed due to the most of the image part is in approximation part. The approximation coefficient is also filter by fuzzy filters and wiener filter separately. Median and moving average based fuzzy filter are used to apply the filtering on probabilistic way. The trapezoidal membership function are used for the filtering . The peak signal to noise ratio (PSNR) and mean square error (MSE) are used as the performance parameter. The Haar wavelet is used with various filters to optimize the performance of denoising. The combination of Haar wavelet and ATMED filter are giving the best denoising result.

References
  1. R C Gonzalez and R . E. Wood Digital image processing Prentice hall, upper saddle river, N . J. ,2nd edition, 2002.
  2. Huipin Zhang,Aria Nosratinia and R. 0. Wells, "Image Denoising Via Wavelet-Domain Spatially Adaptive FIR Wiener Filtering", IEEE Trans. , pp. 2179-2182, 2000.
  3. Xiwen Qin , Yang Yue, Xiaogang Dong and Xinmin Wang , ZhanSheng Tao "An Improved Method of Image Denoising Based on Wavelet Transform" International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE) 2010.
  4. Li Ke, Weiqi Yuan and Yang Xiao, "An Improved Wiener Filtering Method in Wavelet Domain", IEEE Trans. , ICALIP, pp. 1527-1531, August 2008.
  5. Sachin D Ruikar and Dharmpal D Doye "Wavelet Based Image Denoising Technique" (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 3, March 2011.
  6. Li Shixin, Zhang Xinghui and Wang Jianming "A new Local Adaptive Wavelet Image De-noising Method", IEEE Computer Society, ISCCS, pp. 154-156, 2011.
  7. https://www. clear. rice. edu/elec431/projects95/lords/wiener. html
  8. Nevine Jacob and Aline Martin "Image Denoising In the Wavelet Domain Using Wiener Filtering" December 17, 2004.
  9. S. Arunkumar, Ravi Tej Akula, Rishabh Gupta, and M. R. Vimala Devi "Fuzzy Filters to the Reduction of Impulse and Gaussian Noise in Gray and Color Images" International Journal of Recent Trends in Engineering, Vol. 1, No. 1, May 2009.
  10. Ms. Jignasa M. Parmar and Ms. S. A. Patil "Performance Evaluation and Comparison of Modified Denoising Method and the Local Adaptive Wavelet Image Denoising Method" International Conference on Intelligent Systems and Signal Processing (ISSP) 2013.
  11. H. K. Kwan and Y. Cai "Fuzzy filters for image filtering", MWSCAS 45th midwest Symp. Circuits and systems, 2002, pp. III-672-5.
  12. Xiwen Qin , Yang Yue, Xiaogang Dong, Xinmin Wang and ZhanSheng Tao, "An Improved Method of Image Denoising Based on Wavelet Transform", International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE), IEEE, pp. 167-170, 2010.
  13. Israr Hussain and Hujun Yin, "A Novel Wavelet Thresholding Method for Adaptive Image Denoising", IEEE, ISCCSP Malta, pp. 1252-1256,2008.
  14. Stefan Schulte, Valerie De Witte and Etienne E. Kerre, " A Fuzzy Noise Reduction Method For Color Images" IEEE Transactions on image Processing, Vol 16, No. 5, May 2007.
  15. Jin F,Fiegush, P,Winger L et al. , "Adaptive Wiener filtering of noisy images and image sequences", Proceedings of the IEEE International Conference on Image Processing, ICIP,Vol. 3, pp. 349-352, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

ATMED ATMEV MSE PSNR Wavelet Transform.

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