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Blind Restoration Method for Satellite Images using Memetic Algorithm

by Parul Gupta, Rajesh Mehra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 130 - Number 1
Year of Publication: 2015
Authors: Parul Gupta, Rajesh Mehra
10.5120/ijca2015906857

Parul Gupta, Rajesh Mehra . Blind Restoration Method for Satellite Images using Memetic Algorithm. International Journal of Computer Applications. 130, 1 ( November 2015), 20-25. DOI=10.5120/ijca2015906857

@article{ 10.5120/ijca2015906857,
author = { Parul Gupta, Rajesh Mehra },
title = { Blind Restoration Method for Satellite Images using Memetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 130 },
number = { 1 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 20-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume130/number1/23174-2015906857/ },
doi = { 10.5120/ijca2015906857 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:23:52.009995+05:30
%A Parul Gupta
%A Rajesh Mehra
%T Blind Restoration Method for Satellite Images using Memetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 130
%N 1
%P 20-25
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Restoration of degraded satellite images are in demand. The sources of degradation can be aliasing, blur, noise and atmospheric turbulence, which are usually an ill-posed in nature. This paper introduces Memetic algorithm for image restoration. Previous restoration techniques have been investigated, but except for certain special cases the maximum cases solve the resulting criterion approximately only. So there is a requirement of more demanding optimization methods. A Memetic algorithm herein proposed give efficient image representation by using hill climbing method for population initialization and using extended neighborhood search. The algorithm is performed on a quickbird test satellite image for the optimization of result. The optimization codes are written in Matlab. The proposed method shows 25% improvement from degraded image. Competitively our new approach performs better than some best existing methods. to demonstrate the effectiveness of the proposed method, comparisons are given from the existing methods.

References
  1. M. R. Banham and A. K. Katsaggelos, “Digital Image Restoration,” IEEE Signal Processing Magazine, Vol. 14, No.2, pp.24-41, March 1997.
  2. M. Ben-Ezra and S. K. Nayar, “Motion Based Deblurring,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 26, No. 6, pp. 689-698, June 2004.
  3. Swati Sharma, Shipra Sharma and Rajesh Mehra, “Image Restoration using Modified Lucy Ricardson Algorithm in the Presence of Gaussian and Motion Blur,” Advance in Electronic and Electric Engineering, Vol.3, No.8, pp. 1063-1070, 2013 .
  4. Rupinder Verma and Rajesh Mehra, “Area Efficient FPGA Implementation of Sobel Edge Detector for Image Processing Applications,” International Journal of Computer Applications, Vol. 56, No. 16, pp. 7-11, October 2012.
  5. Yanfei He and Shunfeng Wang, “Adaptive Reiprocal Cell based Sparse Representation for Satellite Image Restoration,” Advance Science and Technology Letters, Vol. 49, No. 50, pp. 281-285, 2014.
  6. Nidhi Rastogi and Rajesh Mehra, “Analysis of Savitzky-Golay Filter for Baseline Wander Cancellation in ECG using Wavelets,” International Journal of Engineering Sciences & Emerging Technologies, Vol. 6, Issue 1, pp. 15-23, August 2013.
  7. R.C.Gonzalez and R.E.Woods, “Digital Image Processing Second Edition,” Prentice-Hall India, 2007.
  8. H. F. Shen, Huanfeng, Lijun Du, Liangpei Zhang, and Wei Gong. “A Blind Restoration Method for Remote Sensing Images,” IEEE Geosciences and Remote Sensing Letters, Vol. 9, No. 6, pp. 1137-1141, November 2012.
  9. R Lagendijk “Basic Methods for Image Restoration and Identification”, Handbook of Image and Video processing, Academic Press, 2005.
  10. Stanimirovic, Predrag S., Spiros Chountasis, Dimitrios , Pappas and Igor Stojanovic. “Removal of Blur in Images Based on Least Squares Solutions”, Mathematical Method in Applied Sciences, Vol. 36, Issue 17, pp.2280-2296, November 2013.
  11. Payal Agarwal and Rajesh Mehra, “High Speed CT Image Reconstruction using FPGA,” International Journal of Computer Applications, Vol. 22, No. 4, pp. 7-10, May 2011.
  12. Sugreev Kaur and Rajesh Mehra, “High Speed and Area Efficient 2D DWT Processor Based Image Compression,”: An International Journal on Signal And Image Processing, Vol. 1, No. 2, pp. 22- 31, December 2010.
  13. L. Lucy, “An Iterative Technique for the Rectification of Observed Distributions,” Astronomical Journal., Vol. 79, No. 6, pp. 745–765, June. 1974.
  14. Z. Liu, C. Wang, and C. Luo, “CBERS-1 PSF Estimation and Image Restoration,” International Journal of Remote Sensing, Vol. 8, No. 3, pp. 234–238, September 2004.
  15. J. Papa, N. Mascarenhas, L. Fonseca, and K. Bensebaa, “Convex Restriction Sets for CBERS-2 Satellite Image Restoration,” International Journal of Remote Sensing, Vol. 29, No. 2, pp. 443–458, January,2008.
  16. Y. You and M. Kaveh, “A Regularization Approach to Joint Blur Identification and Image Restoration,” IEEE Transaction on Image Processing, Vol. 5, No. 3,pp. 416–428, March 1996.
  17. R. Pan and S. Reeves, “Efficient Huber–Markov Edge-Preserving Image Restoration,” IEEE Transaction on Image Processing, Vol. 15, No. 12, pp. 3728–3735, December. 2006.
  18. Y. Lou, A. L. Bertozzi, and S. Soatto, “Direct Sparse Deblurring,” Journal of Mathematical Imaging and Vision, Vol. 39, No. 1, pp. 1–12, January. 2011.
  19. M. Zhao,W. Zhang, Z.Wang, and Q. Hou, “Satellite Image Deconvolution Based on Nonlocal Means,” Applied Optics, Vol. 49, No. 32, pp. 6286–6294, November. 2010.
  20. A. Levin, Y. Weiss, F. Durand, and W. T. Freeman, “Efficient Marginal Likelihood Optimization in Blind Deconvolution,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2657–2664, 2011.
  21. G. Ayers and J. Dainty, “Iterative Blind Deconvolution Method and its Applications,” Optics Letters, Vol. 13, No. 7, pp. 547–549, July. 1988.
  22. X. Chen, S. Yang, X.Wang, and Y. Qiao, “Satellite Image Blind Restoration Based on Surface Fitting and Iterative Multishrinkage Method in Redundant Wavelet Domain,” International Journal of Light Electron Optics, Vol. 121, No. 21, pp. 1909–1911, November. 2010.
  23. D. E. Goldberg, “Genetic Algorithms in search, optimization and machine learning,” Addison-Wesley Longman, 1989.
  24. Rakesh Kumar, Sudhir Narula, Rajesh Kumar, “A Population Initialization Method by Memetic Algorithm”, International Journal of Advance Research in Computer Science and software Engineering, Vol. 3, issue 4, pp. 519-523, April 2013.
  25. Na Li and Yuanxiang Li, “Image Restoration using Improved Pariticle Swarm Optimization,” Internatinal Conference on Network Computing and Information Security, pp. 394-397, 2011.
  26. Lingwei Chen, “Blind Image Restoration using Divisional Regularization and Wavelet Technique,” IEEE Fourth International Conference on Natural Computaion, Vol. 5, pp. 476-480, October 2008.
  27. Ganapati Panda, “Improved Adaptive Impulse Noise Suppression,” IEEE International Fuzzy System Conference, pp. 1-4, July 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Memetic Algorithm PSF Extended neighborhood Search PSNR

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