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Image Denoising In Gaussian and Impulsive Noise Based On Block Bidimensional Empirical Mode Decomposition

by Faten Ben Arfia, Mohamed Ben Messaoud, Mohamed Abid
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 10
Year of Publication: 2012
Authors: Faten Ben Arfia, Mohamed Ben Messaoud, Mohamed Abid
10.5120/7388-0215

Faten Ben Arfia, Mohamed Ben Messaoud, Mohamed Abid . Image Denoising In Gaussian and Impulsive Noise Based On Block Bidimensional Empirical Mode Decomposition. International Journal of Computer Applications. 48, 10 ( June 2012), 41-46. DOI=10.5120/7388-0215

@article{ 10.5120/7388-0215,
author = { Faten Ben Arfia, Mohamed Ben Messaoud, Mohamed Abid },
title = { Image Denoising In Gaussian and Impulsive Noise Based On Block Bidimensional Empirical Mode Decomposition },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 10 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 41-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number10/7388-0215/ },
doi = { 10.5120/7388-0215 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:43:46.259882+05:30
%A Faten Ben Arfia
%A Mohamed Ben Messaoud
%A Mohamed Abid
%T Image Denoising In Gaussian and Impulsive Noise Based On Block Bidimensional Empirical Mode Decomposition
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 10
%P 41-46
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we develop an adaptive algorithm for decomposition and filtering of grayscales images. This method is highly adaptive decomposition image called Bidimensional Empirical Mode Decomposition (BEMD) based in blocks. This proposed approach decomposes image into a basis functions named Intrinsic Mode Function (IMF) and residue. This method offers a good result in visual quality but it consumes an important execution time. To overcome this problem we propose a new approach using Block based BEMD method where the input image is subdivided into blocks. Then the conventional BEMD is applied on each of the four blocks separately. This proposed extended method gives a solution in reduction of execution time. This approach shows the good results in the field of image filtering. Denoised image is obtained by summing the residue and the filtered first IMFs (the detail) using a wavelet technique. Experimental results positively show that this proposed methodology removes Gaussian and Impulsive noises from the images.

References
  1. LUKIN, V. , OKTEM, R. , N. PONOMARENKO and EGIAZARIAN, K. , 2007 Image filtering based on discrete cosine transform, Telecommunications and Radio Engineering, 66, No 18, 1685-1701.
  2. DONOHO, D. L AND JOHNSTONE L. M. , 1994 Ideal Spatial Adaptation via Wavelet Shrinkage, Biometrika vol. 81, 425-455.
  3. MALLAT, S. G. , 1989A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol 11, 674-693.
  4. P. PERONA AND J. MALIK, Scale-space and edge detection using anisotropic diffusion, IEEE Trans, Pattern Analysis and Machine Intelligence, Vol. 12, No. 7, pp. 629-639, 1990.
  5. DAMERVAL, C. , MEIGNEN, S. AND PERRIER, V. 2005 A fast algorithm for Bidimensional EMD, IEEE Signal Processing Letters, Vol. 12, 701-704,.
  6. HUANG, NE. , SHEN, Z. , LONG, SR. , WU, MC. , SHIH, HH. , ZHENG, Q. , YEN, N-C. , TUNG, CC. AND LIU, HH. 1998 The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis, Proc R Soc Land Ser A, 903-995.
  7. BEN ARFIA, F. , BEN MESSAOUD, M. AND ABID, M. 2010 A New Image denoising Technique Combining the Empirical Mode Decomposition with a Wavelet Transform Technique 17th International Conference on Systems, Signals and Image Processing.
  8. NUNES, J. C. , BOUAOUNE, Y. , DELECHELLE, E. , NIANG, O. AND BUNEL, PH. 2003 Image analysis by Bidimensional empirical mode decomposition, Image and Vision Computing.
  9. LINDERHED, 2002 A. 2D empirical mode decompositions - in the spirit of image compression, Proc. of SPIE, Vol. 4738, 1-8.
  10. SABRI, A. , KAROUD, M. , TAIRI, H. AND AARAB, 2008 A. Fast Bidimensional Empirical Mode Decomposition Based on an Adaptive Block Partitioning, International Journal of Computer Science and Network Security, VOL. 8.
  11. LINDERHED, A. , 2002 2D empirical mode decomposition in the spirit of image compression , Wavelet and ICA Applications IX, SPIE Proceedings, Orlando, 1-8.
  12. LINDERHED, A. , 2009 Image Empirical Mode Decomposition: a New Tool for Image Processing, Advances in Adaptive Data Analysis, 265-294.
  13. ABDELOUAHED, S. , 2009 Décomposition multimodale empirique bidimensionnelle : Améliorations et Applications. Theses, university of Sidi Mohammed Ben Abdellah Faculty of sciences Dhar el Mehraz Fes, Marooc.
  14. DEMIR, B. , S. ERTUURK, M. K GUULLUU, Hyperspectral Image Classification Using Denoising of Intrinsic Mode Functions, Geoscience and Remote Sensing Letters, IEEE , Volume 8 Issue 2 , 220 - 224 , 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Bemd Bbemd Imf Execution Time Psnr

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