A Blind and Non-Blind Deblurring: An Algorithm for Deblurring based on Residual Whiteness in Images
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10.5120/18261-9197 |
M.sahithya, I.suneetha and N.pushpalatha. Article: A Blind and Non-Blind Deblurring: An Algorithm for Deblurring based on Residual Whiteness in Images. International Journal of Computer Applications 104(13):12-17, October 2014. Full text available. BibTeX
@article{key:article,
author = {M.sahithya and I.suneetha and N.pushpalatha},
title = {Article: A Blind and Non-Blind Deblurring: An Algorithm for Deblurring based on Residual Whiteness in Images},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {104},
number = {13},
pages = {12-17},
month = {October},
note = {Full text available}
}
Abstract
Now a day's deblurring plays a crucial problem in many situations research due to the digital devices popularity such as digital camera, smart phone with camera etc. Aim of the image deblurring is making pictures sharp and useful. In previous methods do not find the perfect solution some disturbances are spectrally white occur in the image deblurring techniques. But In the proposed method compared to the non-blind deblur blind deblur gives the better results for synthetic and real life degradations with and without noise both in single and multiframe scenarios and also evaluate the whiteness in the image in terms of speed and restoration quality to compare the other deblurring techniques this paper yields better results .
References
- J. M. Bivouacs-Dias and M. A. T. Figueiredo, "A newTwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration," IEEE Trans. on Image Processing, vol. 16, pp. 2992 – 3004, 2007.
- G. Chantas, N. Galatsanos, A. Likas, and M. Saunders, "Variational Bayesian image restoration based on a product of t-distributions image prior," IEEE Trans. Image Processing, vol. 17, pp. 1795–805, 2008.
- M. Elad, M. A. T. Figueiredo, and Y. Ma, "On the role of sparse and redundant representations in image processing," Proceedings of the IEEE, vol. 98, pp. 972–982, 2010
- J. -L. Starck, F. Murtagh, and J. Fadili, Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity. CambridgeUniversity Press, 2010.
- A. S. Carasso, "The APEX method in image sharpening and the use of low exponent L´evy stable laws," SIAM Journal of Applied Mathematics, vol. 63, pp. 593–618, 2003
- M. S. Chang, S. W. Yun, and P. Park, "PSF search algorithm for dual-exposure type blurred image," Int. Journal of Applied Science, Engineering and Technology, vol. 4, 2007
- B. Amizic, S. D. Babacan, R. Molina, and A. K. Katsaggelos, "Sparse Bayesian blind image deconvolution with parameter estimation," in European Signal Processing Conference, 2010
- S. D. Babacan, R. Molina, and A. K. Katsaggelos, "Variational Bayesian blind deconvolution using a total variation prior," IEEE Trans. Image Processing, vol. 18, pp. 12–26, 2009.
- G. Golub, M. Heath, and G. Wahba, "Generalized cross-validation as amethod for choosing a good ridge parameter," Technometrics, vol. 21, pp. 215–223, 1979
- Spatial operations, http://zernike. u winnipeg. ca/~s_liao/Courses/7205/Week03 [11 ] J. Y. im, L. S. Kim, S. H Hwang, "An advanced Contrast Enhancement Using Partially Overlapped Sub –Block Histogram Equalization",IEEE Transactions on Circuits and Systems for Videc Technology, Vol. 11, No. 4, pp. 475-484,2001
- M. A. T. Figueiredo and R. D. Nowak, "An EM algorithm for waveletbased image restoration" IEEE Trans. Image Processing, vol. 12, no. 8, pp. 906–916, 2
- M. S. C. Almeida and L. B. Almeida, "Blind deblurring of natural images," in IEEE Int. Conf. Acoustics, Speech, and Signal Processing - ICASSP, 2008, pp. 1261–1264.