IJTEEE
International Journal of Technology Enhancements and Emerging Engineering Research (ISSN 2347-4289)
PREVIOUS PUBLICATIONS



IJTEEE >> Volume 2 - Issue 9, September 2014 Edition



International Journal of Technology Enhancements and Emerging Engineering Research  
International Journal of Technology Enhancements and Emerging Engineering Research

Website: http://www.ijteee.org

ISSN 2347-4289



Video Denoising With Bi-Dimensional EMD Decomposition Along With Wavelet Thresholding

[Full Text]

 

AUTHOR(S)

M. Morshed, R. Ahamad, R. A. Wara, J. Ul Islam, K. Md. A. Rahman

 

KEYWORDS

Keywords : Empirical Mode Decomposition; wavelet transform; Intrinsic Mode Function; discrete cosine transform; Wiener filtering etc.

 

ABSTRACT

ABSTRACT: For analyzing non-linear and non-stationary signals Empirical Mode Decomposition (EMD) is introduced as an adaptive method like wavelet packet best basis decomposition. Huang et. al introduced the empirical mode decomposition (EMD) in signal processing in 1998. In this communication we investigated the performance of video denoising using bi-dimensional EMD along with wavelet thresholding. Compressed video quality is obtained with a satisfactory signal to noise (SNR). Indeed, the reconstructed video frames include residual noise and different realizations of frames plus noise that may produce different number of modes.

 

REFERENCES

[1] J. C. Brailean, R. P. Kleihorst, S. Efstratiadis, A. K. Katsaggelos, and R. L. Lagendijk, “Noise reduction filters for dynamic image sequences: a review” , Proceedings of the IEEE, vol.83, no.9, pp. 1272-1292, Sept. 1995.

[2] R. Dugad and N. Ahuja, “Video denoising by combining Kalman and Wiener estimates”, IEEE International Conference on Image Processing, vol.4, pp. 152-156, Oct. 1999.

[3] F. Dekeyser, P. Bouthemy and P. Perez, “Spatio-temporal Wiener filtering of image sequences using a parametric motion model”, IEEE International Conference on Image Processing, vol.1, pp. 208-211, Sept. 2000.

[4] K. J. Boo and N. K. Bose, “A Motion-Compensated Spatio-Temporal Filter for Image Sequences with Sig-nal-Dependent Noise Images” , IEEE Trans. on Circuits and Systems for Video Technology, vol. 8, no.3, pp.287-298, June 1999.

[5] M. Kazubek, “Wavelet domain image denoising by thre-sholding and Wiener filtering,” IEEE Signal Process Lett. 10(11), 324–326, 2003.

[6] Z. Wang, C. Qu, and L. Cui, “Denoising images using Wiener filter in directionalet domain,” in Proc. Interna-tional Conf. on Computational Intelligence for Modeling Control and Automation, Sydney, NSW Dec. 2006.

[7] P. A. Khazron and I. W. Selesnick, “Spatiotemporal wavelet maximum a posteriori estimation for video denoising,” J. Electron. Imag. 19(4), 043015, 2010.

[8] T. H. Lee,J. Kang, B. C. Song,"Video denoising using overlapped motion compensation and advanced collaborative filtering",Journal of Electronic Imaging 21(2), 023004,Apr.–Jun. 2012.

[9] K. N. Plataniotis, D. Androutsos, A. N. Venetsanopou-los, “Multichannel Filters for Image Proccesing,” Signal Processing: Image Communication 9(2), 143-158, 1997.

[10] H. B. Yin, X. Z. Fang, Z. Wei, X. K. Yang, “An improved motion-compensated 3-D LLMMSE filter with spatiotemporal adaptive filtering support,” IEEE Trans. Circuits Syst. Video Technol. 17(12), 1714–1727, 2007.

[11] V. I. Ponomaryov, H. Montenegro, R. Peralta-Fabi ,"Three-dimensional fuzzy filter in color video sequence denoising implemented on DSP.", Proc. SPIE 8656, Real-Time Image and Video Processing, 86560A, Feb. 2013.

[12] N. E. Huang, Z. Shen, S.R. Long, et al. "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis." Proc. Royal Soc. London A, 454 (1971), 903-995, 1998.

[13] T. Wang, "Research on EMD Algorithm and its Applica-tion in Signal Denoising". Ph.D. dissertation, Harbin Engineering University, 2010.

[14] O. A. Omitaomu,V. A. Protopopescu, and A. R. Ganguly, "Empirical mode decomposition technique with conditional mutual information for denoising operational sensor data", IEEE Sensors Journal, 11(10), 2565-2575, 2011.

A. O. Boudraa, J. C. Cexus, and Z. Saidi. "EMD-based signal noise reduction". International Journal Signal Processing, 1(1), 33-37, 2004.

[15] N.E. Huang and al. “ The empirical mode decomposition and Hilbert spectrum for nonlinear and non - stationary time series analysis”, Proc. Royal Society, 454(1971):903 – 995, 1998.

[16] W. Wei, P. Hua,"A New Denoising Approach Based on EMD",Proc. SPIE 9159, Sixth International Conference on Digital Image Processing (ICDIP), 91591L, Apr. 2014.

[17] J.C. Nunes, Y. Bouaoune, E. Delechelle, O. Niang,P. Bunel,"Image analysis by bidimensional empirical mode decomposition",Image and Vision Computing, 21(12), 1019–1026, Nov. 2003.

[18] F. B. Arfia, A. Sabri, M. B. Messaoud, M. Abid, "The Modified Bidimensional Empirical Mode De composition for Color Image Decomposition", Proceedings of the World Congress on Engineering (WCE), Vol II, Jul. 2011.

A. Sabri, M. Karoud, H. Tairi and A. Aarab “Fast Bi-dimensional Empirical Mode Decomposition Based on an Adaptive Block Partitioning” IJCSNS International Journal of Computer Science andNetwork Security, 8(11), 2008.

[19] D. L. Donoho, I. M. Johnstone, "Ideal spatial adaptation via wavelet shrinkage", Biometrika, 81(3), 425-455, 1994.

[20] S. Mallat, A wavelet tour of signal processing, Academic Press, London, 1998.

A. Pizurica, V, Zlokolica, W. Philips , "Noise reduction in video sequences using wavelet-domain and temporal filtering", Proc. SPIE 5266, Wavelet Applications in Industrial Processing, 48, Feb. 2004.

[21] D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inform. Theory 41, pp. 613–627, May 1995.

[22] http://telin.ugent.be/~vzlokoli/Results_J/noisy/

[23] M. Morshed, M. M. Nabi, N. B. Monjur"Frame By Frame Digital Video Denoising Using Multiplicative Noise Model",IJTEEE, 2(7), Jul. 2014.
[24] Y. Kopsinis, S. McLaughlin, "Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding", IEEE Transactions on Signal Processing, 57(4), Apr. 2009.