Cover
Vol. 9 No. 1 (2013)

Published: December 31, 2013

Pages: 1-15

Original Article

Restoration of Noisy Blurred Images Using MFPIA and Discrete Wavelet Transform

Dunia S. Tahir 1
1Basrah University, Engineering College, Computer Department
History
  • Received: October 31, 2013
  • Available Online: December 31, 2013
DOI

https://doi.org/10.37917/ijeee.9.1.1

Abstract

In this paper, image deblurring and denoising are presented. The used images were blurred either with Gaussian or motion blur and corrupted either by Gaussian noise or by salt & pepper noise. In our algorithm, the modified fixed-phase iterative algorithm (MFPIA) is used to reduce the blur. Then a discrete wavelet transform is used to divide the image into two parts. The first part represents the approximation coefficients. While the second part represents the detail coefficients, that a noise is removed by using the BayesShrink wavelet thresholding method.

Keywords

References

  1. S. E. Umbaugh , “Computer Vision and USA ,1998.
  2. C. H. Vannimenus, “Denoising Images withWavelets”,French,2003. http://www.chez.com/petitspirou/bristol/denoisi ng/report/wavelet.pdf.
  3. C. Taswell, “The What, How, and Why of Wavelet Shrinkage Denoising”, Technical Report, Stanford, CA, 1999.
  4. A. Smirnov and M. Ginzburg, “Iterative http://www.cs.technion.ac.il/Labs/Is1/Project/Pr ojects_done/Vision/Classes/DIP_1998/Iteration _Restoration.
  5. MATLAB R2011a, “Image Processing Toolbox”
  6. M. Ghazel, “Adaptive Fractal and Wavelet Image Denoising”, Ph. D. Thesis Submitted to The Electrical and Computer Engineering, University of Waterloo, Ontario, Canada, 2004.
  7. Sachin D and Dharmpal D, “Wavelet Based Journal of Advanced Computer Science and Applications,Vol. 2, No. 3, March 2011.
  8. MATLAB R2011a, “Wavelet Toolbox”. http://www.mathwork.com.
  9. N.Sundaram,“ Wavelet-Based Denoi- sing and Watermarking in Images”, Image Processing Project # 9. http://astro.temple.edu/~nithyas/ee740_nithya_r eport9.pdf.
  10. D. L. Donoho, “De-noising by SoftThresholding”, IEEE Trans. Infor. Theory , Vol. 41, pp. 613-627, 1995.
  11. D. L. Donoho and I. M. Johnstone, “ Adapting to Unknown Smoothness via Wavelet Shrinkage”, Journal of American Statistical Association , 90 (432): 1200-1224, December 1995.
  12. A. Antoniadis and J. Bigot, “Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study”, Journal of Statistical Software , Vol. 6, 2001.
  13. R. Rangarajan, R. Venkataramanan and S. Shah, “Image Denoising Using Wavelets ”, December 16, 2002.
  14. S. G. Chang, B. Yu and M. Vetterli, “Adaptive Wavelet Thresholding for Image Denoising and Compression”, IEEE Trans. 1546, September 2000.
  15. F. Zhe Ren and H. Zhou, “The Fixed – Phase Iterative Algorithm Recovery of Blurred Image”, ICSP, 2000.
  16. H. A. Jwad, “ A Novel Approach for Restoration of Blurred Images ”, M. Sc. Thesis Submitted to The Department of The Computer Engineering, University of Basrah , May 2004
  17. “Digital Image Processing in Natural Sciences and Medicine”. http://as.ch/DIP/Manual/1-WinterSemester/DIP-Part-01(6-11).pdf.
  18. J. G. Brankov, J. Djordjeric, M. N. Wernick and N. P. Galatsanos, “ Tomographic Reconstruction for System with Partially-Known Blur”, Department of Electrical and Computer Engineering, Illinois
  19. P. Nikolas, “Multichannel Regularized Iterative Restoration of Motion Compensated Image Sequences”, M. Sc. Thesis Submitted to The Department of Electrical and Computer Engineering, University of Northwestern, March 1995.
  20. X. Miao, “ Image Restoration:Removal of Blur Caused by Uniform Linear Motion”, University of California at Berkeley , 2000.
  21. K. K. Aggelos, “Iterative Image Restoration Algorithm”, Optical Engineering, Vol. 28, No. 7, pp. 735-748, July 1989.
  22. H. H. Abdul Zahrah, “ Encryption Using Wavelet Coded Image Data ”, M. Sc. Thesis Submitted to The Department of The Computer Engineering, University of Basrah, June 2004.