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Denoising of Digital Images using Consolidation of Edges and Separable Wavelet Transform

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Bhumika A. Neole, Bhagyashree V. Lad, K. M. Bhurchandi
10.5120/ijca2017914512

Bhumika A Neole, Bhagyashree V Lad and K M Bhurchandi. Denoising of Digital Images using Consolidation of Edges and Separable Wavelet Transform. International Journal of Computer Applications 168(10):24-32, June 2017. BibTeX

@article{10.5120/ijca2017914512,
	author = {Bhumika A. Neole and Bhagyashree V. Lad and K. M. Bhurchandi},
	title = {Denoising of Digital Images using Consolidation of Edges and Separable Wavelet Transform},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {168},
	number = {10},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {24-32},
	numpages = {9},
	url = {http://www.ijcaonline.org/archives/volume168/number10/27912-2017914512},
	doi = {10.5120/ijca2017914512},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Denoising is still a challenging area of research due to its commercial and technical applications. We present a novel approach to image denoising using edge profile detection and edge preservation in spatial domain in presence of zero mean additive Gaussian noise. A Noisy image is initially preprocessed using the proposed local edge profile detection and subsequent edge preserving filtering in spatial domain followed further by the modified threshold bivariate shrinkage algorithm. The proposed technique does not require any estimate of standard deviation of noise (σ) present in the image. Performance of the proposed algorithm is presented in terms of PSNR and SSIM on a variety of test images containing a wide range of σ starting from 15 to 100. The performance of the proposed algorithm is better than NL means and Bivariate Shrinkage while it’s comparable with BM3D.

References

  1. L. Shao, R. Yan, X. Li, and Y. Liu, “From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms,” Cybernetics, IEEE Transactions on, vol. 44, no. 7, pp. 1001–1013, July 2014.
  2. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” Image Processing, IEEE Transactions on, vol. 16, no. 8, pp. 2080–2095, Aug 2007
  3. Jin Guoying ,ChenHongye, “Wavelet Local Denoise Reconstruction Algorithm based on non-separable MRA”, International Forum on Information Technology and Applications, 2010.
  4. V.NagaPrudhvi Raj and Dr. T Venkateswarlu, " Denoising of Medical Images Using Undecimated Wavelet Transform ", IEEE, pp. 483-488, 2011.
  5. R. R. Coifman & D. L. Donoho, “Translation invariant denoising in Wavelets and Statistics”, Springer-Verlag, Eds. New York, 1995, pp.125–150.
  6. N. G. Kingsbury, “A dual-tree complex wavelet transform: a new efficient tool for image restoration and enhancement,” Proc. EUSIPCO’98, Rhodes, Sept. 1998, pp. 319-322.
  7. P.R. Hill, A. Achim, D.R. Bull and M.E. Al-Mualla, “Dual-tree complex wavelet coefficient magnitude modelling using the bivariate Cauchy–Rayleigh distribution for image denoising”, Signal Processing 105, Elsevier, 2014, pp. 464–472.
  8. M. Crouse, R. Nowak, and R. Baraniuk, “Wavelet-based statistical signal processing using hidden Markov models”, IEEE Trans. Signal Processing, vol. 46, No.4,pp. 886–902, April 1998.
  9. Semler, L., & Dettori, L. “A Comparison of Wavelet-Based and Ridgelet-Based Texture Classification of Tissues in Computed Tomography”, Proceedings of International Conference on Computer Vision Theory and Applications, 2006, pp.1-5.
  10. G. Y. Chen, T. D. Bui and A. Krzyzak, “Rotation Invariant Pattern Recognition using Ridgelet, Wavelet Cycle-spinning and Fourier Features,” Pattern Recognition vol 38, 2005, pp. 2314- 2322.
  11. G. Y. Chen and B. Kegl, “Complex Ridgelets for Image Denoising” Department of Computer science and Operations Research, Unversity of Montreal, CP 6128 succ. Centre-Ville, Montreal, Quebec, Canada H3C 317.
  12. G. Y. Chen and B. Kegl. “Image denoising with complex ridgelets,” Pattern Recognition, Elsevier, vol. 40, pp. 578-585, 2007.
  13. Rajlaxmi Chouhan, Rajib Kumar Jha and Prabir Kumar Biswas, " Image Denoising using Dynamic Stochastic Resonance in Wavelet domain ", 12th International Conference on Intelligent Systems Design and Applications (ISDA), IEEE, pp. 58-63, 2012.
  14. Baudes A, BartomeuColl, Jean Michel Morel, “Image Denoising by non-Local Averaging” Proceedings of ICASSP 2005, IEEE, 0-7803-8874-7, 2005.
  15. A. Buades, B. Coll, and J. M. Morel, "On Image Denoising Methods," Technical Report 2004-15, CMLA, 2004, pp.1-40.
  16. A. Buades, B. Coll, J.M. Morel "A Non local algorithm for image denoising", IEEE Computer Vision and Pattern Recognition, Vol. 2, pp.60-65, 2005.
  17. Neelam Mehala, RatnaDahiya, “A Comparative Study of FFT, STFT and Wavelet Techniques for Induction Machine Fault Diagnostic Analysis”, Proc. of the 7th WSEAS Int. Conf. on Computation al Intelligence, Man-Machine Systems and Cybernetics (CIMMACS), 2008, pp. 203-208.
  18. David L. Donoho and Iain M. Johnstone. “Adapting to Unknown Smoothness via Wavelet Shrinkage.” Journal of the American Statistical Association, Vol. 90, No. 432, pp. 1200-1224, Dec. 1995.
  19. E. P. Simoncelli, “Bayesian denoising of visual images in the wavelet domain,” in BayesianInference in Wavelet Based Models, P. Müller and B. Vidakovic, Eds. New York: Springer-Verlag, 1999.
  20. LeventSendur and Ivan W. Selesnick, “Bivariate Shrinkage With Local Variance Estimation”, IEEE Signal Processing Letters, Vol. 9, No. 12, December 2002.
  21. E. LePennec and S. Mallat, “Image compression with geometrical wavelets”, In IEEE Int. Conf. Image Processing, Vancouver, Canada, September 2000.
  22. WangCan, SuWeimin, GuHong, ShaoHua, " Edge Detection of SAR Images using Incorporate Shift-Invariant DWT and Binarization Method ", ICSP2012 Proceedings IEEE, pp. 745-748, 2012.
  23. D. Marr and E. Hildreth, “Theory of edge detection,” Proc. Royal Society of London, B 207 pp.187–217, 1980.
  24. G. Chen, T. Bui, and A. Krzyzak, “Image denoising using neighbouring wavelet coefficients,” in Acoustics, Speech, and
  25. Signal Processing, 2004. (ICASSP ’04). IEEE International Conference on, vol. 2, May 2004, pp. ii–917–20 vol.2
  26. L. Sendur and I. Selesnick, “Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency,” Signal Processing, IEEE Transactions on, vol. 50, no. 11, pp. 2744–2756, Nov. 2002.
  27. L. Sendur and I. Selesnick, “Bivariate shrinkage with local variance estimation,” Signal Processing Letters, IEEE, vol. 9, no. 12, pp. 438–441, Dec. 2002.
  28. A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy, "A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising," IEEE Trans. Image Processing, vol. 11, pp. 545—557, 2002.
  29. “Digital Image Processing,” R. C. Gonzales and R. E. Woods, 3rd ed., Pearson Education Inc., 2012, pp. 350-404 .
  30. T. D. Bui and G. Y. Chen,“Translation invariant denoising using multiwavelets,” IEEE Transactions on Signal Processing, vol.46, no.12, pp.3414-3420, 1998.
  31. S. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Processing, vol. 9, pp. 1532–1546, Sept. 2000.
  32. S. G. Chang, B. Yu, and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising,” IEEE Trans. ImageProcessing, vol. 9, pp. 1522–1531, Sept. 2000.
  33. Firoiu I, Isar A, Isar D , “A Bayesian approach of wavelet based image denoising in a hyperanalytic multi-wavelet context”. WSEAS Trans Signal Process 6:155–164, 2010.
  34. Yin M, Liu W, Zhao X, Guo Q-W, Bai R-F , “ Image denoising using trivariate prior model in nonsubsampled dual-tree complex contourlet transform domain and non-local means filter in spatial domain”. Opt Int J Light Electron Opt 124:6896–6904, 2013.
  35. D. L. Donoho and I. M. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage," J. Amer. Stat. Assoc., vol. 90, pp. 1200—1224, 1995.
  36. H. Guo. Theory and Applications of the Shift-Invariant, Time-Varying and Undecimated Wavelet Transforms.Master's Thesis, Dept. ECE, Rice University, Houston, TX, 1995
  37. A. Gyaourova, C.Kamath, IK Fodor , “Undecimated wavelet transforms for image de-noising” November 19, 2002

Keywords

DWT, Local profile edge detection, Bivariate Shrinkage