Satellite Image Enhancement Using Contrast Limited Adaptive Histogram Equalization

Authors

  • Shaik Asha  M.Tech, Electronics and Communication Engineering Department, Sri Venkateswara University, Tirupathi, Andhra Pradesh, India
  •   Department of Electronics and Communication Engineering Sri Venkateswara University, Tirupathi, Andhra Pradesh, India
  • Dr. G. Sreenivasulu  

Keywords:

Additive Noise, Weiner Deconvolution, Genetic Approach, Image Restoration, PSNR, SNR

Abstract

The satellite Images that are captured from the satellites are blurred by optical system and atmospheric effects and also corrupted by additive noise, the image restoration method known as Weiner deconvolution intervenes to estimate from the degraded image an image as close as possible to the original image . Recently different approaches have been used to reduce the noise in the satellite images. This paper proposed based on genetic approach to the Weiner deconvolution for the satellite image restoration. Our future work is based on different types of filters used to remove the noise completely from the satellite images. The performance can be evaluated using the metrics like PSNR,SNR and comparing it with the existing approaches. Experimental results give the better performance than the other methods and provide valid and accurate results.

References

  1. R. Molina, J. Nunez, F. J. Cortijo, and J. Mateos, "ImageRestoration in Astronomy: A Bayesian Perspective," IEEESignal Processing Magazine, vol. 18, no. 2, pp. 11–29, 2001.
  2. M. R. Banham and A. K. Katsaggelos, "Digital ImageRestoration," IEEE Signal Processing Magazine, vol. 14,no. 2, pp. 24–41, 1997.
  3. R. Dash and B. Majhi, "Motion blur parameters estimationfor image restoration," Optik-International Journal for Lightand Electron Optics, vol. 125, no. 5, pp. 1634–1640, 2014.
  4. A. D. Hillery and R. T. Chin, "Iterative Wiener Filters forImage Restoration," IEEE Transactions on Signal Processing,vol. 39, no. 8, pp. 1892–1899, 1991.
  5. M. Zhao, W. Zhang, Z. Wang, and Q. Hou, "Satellite imagedeconvolution based on nonlocal means," Applied Optics,vol. 49, no. 32, pp. 6286–6294, 2010.
  6. A. Jalobeanu, L. Blanc-F´eraud, and J. Zerubia, "SatelliteImage Deblurring Using Complex Wavelet Packets," InternationalJournal of Computer Vision, vol. 51, no. 3, pp. 205–217, 2003.
  7. P. Campisi and K. Egiazarian, Blind Image Deconvolution:Theory and Applications. CRC Press, 2007.
  8. Y. Li and K. C. Clarke, "Image deblurring for satellite imageryusing small-support-regularized deconvolution," ISPRSJournal of Photogrammetry and Remote Sensing, vol. 85, pp.148–155, 2013.
  9. K.-F. Man, K. S. Tang, and S. Kwong, Genetic Algorithms: Concepts and Designs. Springer Science & Business Media, 2012.
  10. M. Mitchell, an Introduction to Genetic Algorithms. MITPress, 1998.
  11. R. Poli, W. B. Langdon, and N. F. McPhee, A Field Guide toGenetic Programming. Lulu Enterprises, 2008.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-02-28

Issue

Volume 4, Issue 1, January-February-2018

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shaik Asha, , Dr. G. Sreenivasulu "Satellite Image Enhancement Using Contrast Limited Adaptive Histogram Equalization " International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 1, pp.1070-1075, January-February-2018.