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Reseach Article

Article:Qualitative and Quantitative Evaluation of Image Denoising Techniques

by Charandeep Singh Bedi, Dr. Himani Goyal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 8 - Number 14
Year of Publication: 2010
Authors: Charandeep Singh Bedi, Dr. Himani Goyal
10.5120/1313-1775

Charandeep Singh Bedi, Dr. Himani Goyal . Article:Qualitative and Quantitative Evaluation of Image Denoising Techniques. International Journal of Computer Applications. 8, 14 ( October 2010), 31-34. DOI=10.5120/1313-1775

@article{ 10.5120/1313-1775,
author = { Charandeep Singh Bedi, Dr. Himani Goyal },
title = { Article:Qualitative and Quantitative Evaluation of Image Denoising Techniques },
journal = { International Journal of Computer Applications },
issue_date = { October 2010 },
volume = { 8 },
number = { 14 },
month = { October },
year = { 2010 },
issn = { 0975-8887 },
pages = { 31-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume8/number14/1313-1775/ },
doi = { 10.5120/1313-1775 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:57:24.097590+05:30
%A Charandeep Singh Bedi
%A Dr. Himani Goyal
%T Article:Qualitative and Quantitative Evaluation of Image Denoising Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 8
%N 14
%P 31-34
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Digital Imaging plays important role in major areas of life such as clinical diagnosis etc. But it faces problem of speckle noise. Speckle noise is referred as ‘texture’ in medical literature and it may contain useful diagnostic information. Speckle has a negative impact on ultrasound images, as the texture does not reflect the local echogenicity of the underlying scatterers. Physicians generally prefer original noisy images, more willingly than the smoothed versions, even if they are more sophisticated, can destroy some relevant image details. Thus, it is essential to develop noise filters, which can preserve the features that are of interest to the physician. One of the most prevalent cases is distortion due to additive white Gaussian noise, which can be caused by poor image acquisition or by transferring of the image data in noisy communication channels. Moreover there is a long list of image denoising techniques. But problem is that which technique is to be used and for what kind of format. In this paper, we have discussed various spatial filters in chapter 1. The comparison of the results gives the conclusion and the future scope of the discussion.

References
  1. Achim A., Bezerianos A., Tsakalides, P. (2001), “Wavelet-based ultrasound image denoising using an alpha-stable prior probability model”, International Conference on Image processing, Vol.2, Issue 7-10, pp.221-224.
  2. Achim, A., and Bezerianos, A. (2001), “Novel Bayesian Multiscale Method for Speckle Removal in Medical Ultrasound Images”, IEEE Transactions on Medical Imaging, Vol. 20, No. 8, pp.772-783.
  3. Arici, T., Dikbas, S. and Altunbasak, Y. (2009), “A Histogram Modification Framework and Its Application for Image Contrast Enhancement”, IEEE Transactions on Image Processing, Vol.18, No. 9, pp. 1921-1935.
  4. Arivazhagan, S., Deivalakshmi, S., Kanan, K., Gajbhiye, B., Muralidhar, C., Lukose, S. and Subramanian, M. (2007),“Performance Analysis of Wavelet Filters for Image Denoising”, Advances in Computational Sciences and Technology, ISSN 0973-6107, Vol. 1, No. 1, pp. 1-10.
  5. Chang, S., Yu, B., and Vetterli, M. (2000), “Adaptive Wavelet Thresholding for Image Denoising and Compression”, IEEE Transactions on Image Processing, Vol. 9, No. 9, pp. 1532-1546.
  6. Chang, C., Du, Y., Wang, J., Guo, S. and Thouin, P.D. (2006), “Survey and comparative analysis of entropy and relative entropy thresholding techniques”, IEEE proceedings on Vision, Image and Signal Processing, Vol.153, Issue 6, pp. 837-850.
  7. Chang, S., Yu, B. and Vetterli, M. (2000), “Spatially adaptive wavelet thresholding with context modeling for image denoising”, IEEE Transactions on Image Processing, Vol.9, No.9, pp. 1522-1531.
  8. Chen, G.Y., Bui, T.D. and Krzyzak, A. (2004), “Image denoising using neighbouring wavelet coefficients” IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 2, pp. 917-920.
  9. Gonzalez, R. and Woods, R. (2006), “Digital Image Processing with MATLAB”, Pearson Prentice Hall, New Delhi, pp. 417-425.
  10. Goossens, B., Pizurica, A. and Philips, W. (2009), “Image denoising using mixtures of projected Gaussian scale mixtures”, IEEE Transactions on Image Processing, Vol.18, Issue. 8, pp.1689-1702.
  11. Guo, H., Odegard, J.E., Lang, M., Gopinath, R.A., Selesnick, I., and Burrus, C.S. (1994), “Speckle reduction via wavelet shrinkage with application to SAR based ATD/R,” Technical Report CML TR94-02, CML, Rice University, Houston.
  12. Guo, Y., Cheng, H., Tian, J. and Zhang, Y. (2009), “A Novel Approach to Speckle Reduction in Ultrasound Imaging”, Ultrasound in Medicine & Biology, Vol.35, Issue 4, pp. 628-640.
  13. Gupta, S., Chauhan, R. and Sexena, S. (2004), “A Wavelet-Based Statistical Approach for Speckle Reduction in Medical Ultrasound Images”, Medical and Biological Engineering & Computing, Vol. 42, No.2, pp. 189-192.
  14. Gupta, S., Chauhan, R.C., and Saxena, S.C. (2005), “A robust multi-scale non-homomorphic approach to speckle reduction in medical ultrasound images”, IEE J. Int. Fed. Med. Biol. Eng. 152 (1) pp.129–135.
  15. Hamza, A. and Krim, H. (2001), “Image Denoising: A Nonlinear Robust Statistical Approach”, IEEE Transactions on Signal Processing, Vol. 49, No. 12, pp. 3045-3054.
  16. Jain, A. (1989), “Fundamentals of digital image processing”, Prentice-Hall Inc., Englewood Cliffs, New Jersey.
  17. Kaur, L., Gupta, S. and Chauhan, R. (2002), “Image Denoising Using Wavelet Thresholding”, Third Indian Conference on Computer Vision, Graphics and Image Processing, Ahmedabad, pp.1-4.
  18. Lian, N.X., Zagorodnov, V. and Tan, Y.P.(2005), “Color image denoising using wavelets and minimum cut analysis” IEEE Signal Processing Letters, Vol. 12, Issue 11, pp.741-744.
  19. Mastriani, M. (2009), “Denoising and Compression in Wavelet Domain via Projection onto Approximation Coefficients” International Journal of Signal Processing Vol.5, No.1, pp.20-30.
  20. Mastriani, M., and Giraldez, A.E. (2005), “Smoothing of coefficients in wavelet domain for speckle reduction in Synthetic Aperture Radar images” ICGST-GVIP Journal, Vol.5. Issue 6. pp. 1-8.
  21. Mohideen, S., Peruma, S., Sathik, M. (2008), “Image De-noising using Discrete Wavelet transform”, International Journal of Computer Science and Network Security, Vol. 8, No.1, pp.213-216.
  22. Nadernejad, E., Karami, M.R., Sharifzadeh, S and Heidari, M. (2009), “Despeckle Filtering in Medical Ultrasound Imaging”, Contemporary Engineering Sciences, Vol. 2, No.1, pp.17 – 36.
  23. Pizurica, A., Wink, A., Vansteenkiste, E., Philips and W. Roerdink, B.T.M (2006), “A Review of Wavelet Denoising in MRI and Ultrasound Brain Imaging,” Current Medical Imaging Reviews, Vol. 2, No. 2, pp. 247–260.
  24. Pizurica, A., Philips, W., Lemahieu, I., and Acheroy, M. (2003), "A Versatile Wavelet Domain Noise Filtration Technique for Medical Imaging,” IEEE, Transactions on Medical Imaging, vol. 22, No. 3, pp. 323—331.
  25. Saad, A. (1996), “Speckle filtering in SAR images by contrast modification, comparison with a large class of filters”, Annals of telecommunications, Vol.51, No. 5, pp. 233-244.
  26. Sudha, S., Suresh, G. and Sukanesh, R. (2009), “Speckle noise reduction in ultrasound images using context-based adaptive wavelet thresholding”, IETE journal, Vol. 55, pp 135-143.
  27. Watson, A.B., Yang, G.Y., Joshua A. Solomon, J.A. and Villasenor, J. (1997),“Visibility of Wavelet Quantization Noise” IEEE Transactions on Image Processing, Vol. 6, No. 8, pp.1164¬ 1175.
  28. Xuli Z., Laine, A.F., Geiser, E.A. (1998), “Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing”, IEEE Transactions on Medical Imaging, Vol. 17 No.4, pp. 532–540.
  29. Zhong, S. and Cherkassky, V. (2000), “Image denoising using wavelet thresholding and model selection”, International conference on Image Processing, Vol.3, pp. 262-265.
  30. Sudha, S., Suresh, G.R., and Sukanesh, R. (2009), “Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance” International Journal of Computer Theory and Engineering, Vol. 1, No. 1, pp.1793-8201.
Index Terms

Computer Science
Information Sciences

Keywords

Digital Image Processing Denoising Speckle noise Wavelet transform Spatial filters