CFP last date
20 March 2024
Reseach Article

A New Methodology for Blind Image Deconvolution

by L. Jimson, P. Senthil, D. Chandrakanth
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 43 - Number 20
Year of Publication: 2012
Authors: L. Jimson, P. Senthil, D. Chandrakanth
10.5120/6216-8686

L. Jimson, P. Senthil, D. Chandrakanth . A New Methodology for Blind Image Deconvolution. International Journal of Computer Applications. 43, 20 ( April 2012), 1-5. DOI=10.5120/6216-8686

@article{ 10.5120/6216-8686,
author = { L. Jimson, P. Senthil, D. Chandrakanth },
title = { A New Methodology for Blind Image Deconvolution },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 43 },
number = { 20 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume43/number20/6216-8686/ },
doi = { 10.5120/6216-8686 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:33:52.382853+05:30
%A L. Jimson
%A P. Senthil
%A D. Chandrakanth
%T A New Methodology for Blind Image Deconvolution
%J International Journal of Computer Applications
%@ 0975-8887
%V 43
%N 20
%P 1-5
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In image processing, the elimination of noise is a highly difficult in research area. It is one of the most important powerful technologies that will form science and engineering in the twenty first century. The broad knowledge, digital image processing is any shape of information processing used for which both input and output are images, such as photographs. Image restoration method is mainly used to eliminate the inescapable distortions and noise that leave into an image at some point in the image capture process. Direct image restoration difficulty is briefly revisited subsequently a recent method based on inverse filtering for the perfect image restoration in noiseless case is proposed. Noisy case is addressed by means of introducing a regularization term into the objective function in order to avoid the noise amplification. The filter recognition problem is considered in the Multichannel context. A new strong solution to estimate the degradation matrix filter is then derived and used in combination with a total variation approach to restore the original image.

References
  1. G. A. Showman and J. H. McClellan, "Blind polarimetric qualization of ultrawideband synthetic aperture radar imagery," presented at the Int. Conf. Image Processing, 2000.
  2. A. Jalobeanu, L. Blanc-Féraud, and J. Zerubia,"" "Hyperparameter estimation for satellite image restoration using a mcmc maximum likelihood method," Pattern Recognit. , vol. 35, pp. 341–352, 2002.
  3. Y. V. Zhulina, "Multiframe blind deconvolution of heavily blurred astronomical images," Appl. Opt. , vol. 45, no. 28, pp. 7342–7352, Oct. 2006.
  4. S. Filip, ? S. Stanislava, F. Jan, and S. Tomá?s, "Multichannel blind deconvolution of the short-exposure astronomical images," in Proc. Int. Conf. Pattern Recognition, Sep. 2000, vol. 3, pp. 53–56.
  5. M. Vrhel and B. L. Trus, "Multi-channel restoration of electron micrographs," in Proc. Int. Conf. Image Processing, Oct. 1995, vol. 2, pp. 516–519.
  6. H. J. Trussel, M. I. Sezan, and D. Tran, "Sensitivity of color LMMSE restoration of images to the spectral estimation," IEEE Trans. Signal Process, vol. 39, no. 1, pp. 248–252, Jan. 1991.
  7. U. A. Al Suwailem and J. Keller, "Multichannel image identification and restoration using continuous spatial domain modeling," presented at the Int. Conf. Image Processing, Oct. 1997.
  8. F. Sroubek and J. Flusser, "Multichannel blind deconvolution of spatially misaligned images," IEEE Trans. Image Process. , vol. 14, no. 7, pp. 874–883, Jul. 2005.
  9. N. P. Galatsanos, A. K. Katsaggelos, R. T. Chin, and A. D. Hillery, "Least squares restoration of multichannel images," IEEE Trans. Acoust. , Speech, Signal Process. , vol. 39, no. 10, pp. 2222–2236, Oct. 1991.
  10. F. Sroubek and J. Flusser, "Multichannel blind iterative image restoration," IEEE Trans. Image Process. , vol. 12, no. 9, pp. 1094–1106, Sep. 2003.
  11. C. A. Ong and J. A. Chambers, "An enhanced NAS-RIF algorithm for blind image deconvolution," IEEE Trans. Image Process. , vol. 8, no. 7, pp. 988–992, Jul. 1999.
  12. W. Souidene, K. Abed-Meraim, and A. Beghdadi, "Deterministic techniques for multichannel blind image deconvolution," presented at the Proc. ISSPA, Aug. 2005.
  13. L. Tong and Q. Zhao, "Joint order detection and blind channel estimation by least squares smoothing," IEEE Trans. Signal Process. , vol. 47, no. 9, pp. 2345–2355, Sep. 1999.
  14. E. Fishler, A. Haimovich, R. Blum, D. Chizhik, L. Cimini, and R. Valenzuela, "MIMO radar: An idea whose time has come," presented at the IEEE Radar Conf. , Apr. 2004.
  15. B. Hassibi, B. M. Hochwald, and T. L. Marzetta, "Multi-antenna wireless communications—From theory to algorithms," presented at the IEEE ICASSP, May 2002.
  16. D. G. Karakos and P. E. Trahanias, "Generalized multichannel imagefiltering structures," IEEE Trans. Image Process. , vol. 6, no. 7, pp. 1038–1045, Jul. 1997.
  17. Wided Souidene, Karim Abed-Meraim, Azeddine Beghdadi "A new look to multichannel blind image deconvolution" IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 18, NO. 7, JULY 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Automatic Color Enhancement (ace) Image Restoration Human Visual System (hvs)