We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Image Restoration for Shift Invariant Blurs-A Survey

by Mayana J. Shah, Upena D. Dalal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 70 - Number 12
Year of Publication: 2013
Authors: Mayana J. Shah, Upena D. Dalal
10.5120/12015-8048

Mayana J. Shah, Upena D. Dalal . Image Restoration for Shift Invariant Blurs-A Survey. International Journal of Computer Applications. 70, 12 ( May 2013), 22-28. DOI=10.5120/12015-8048

@article{ 10.5120/12015-8048,
author = { Mayana J. Shah, Upena D. Dalal },
title = { Image Restoration for Shift Invariant Blurs-A Survey },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 70 },
number = { 12 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 22-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume70/number12/12015-8048/ },
doi = { 10.5120/12015-8048 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:32:41.438235+05:30
%A Mayana J. Shah
%A Upena D. Dalal
%T Image Restoration for Shift Invariant Blurs-A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 70
%N 12
%P 22-28
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Even though Image Restoration being a conventional problem in image processing, is still a demanding area and has always attracted the interest of research community. Image restoration algorithms are important as it serves a wide range of real world applications such as astronomy, medical imaging and photo editing are just a few which demands a good quality image for further high level processing. Image restoration methods aim to reduce the degradations that have occurred while the digital image was being obtained. All natural images have gone through some sort of degradation when they are acquired, processed or displayed because of sensor noise, camera misfocus, blur caused by relative motion between object & camera, atmospheric turbulence and others. The paper deal with restoration of images degraded by linear space-invariant blurs. Paper presents mathematical modeling of linear shift-invariant image formation process, possible sources of degradation, and reviews some fundamental & specific methods of restoration.

References
  1. Banham, M. R. and Katsaggelos, A. K, " Digital image restoration ", IEEE signal processing magazine, pp 24-41, 1997.
  2. Bahadir Kursat Gunturk. , and Xin Li, "Image Restoration: Fundamentals and Advances. " CRC press, September 11, 2012
  3. Jiang, X. , Cheng, D. C. , Wachenfeld, S. , Rothaus, K. , " Motion Deblurring", University of Muenster, Department of Mathematics and Computer Science, 2005
  4. Dr. Tania Stathaki, "Image Restoration", Imperial College of Science Technology and Medicine, Department of Electrical and Electronic Engineering, 2012
  5. Alan C. Bovik, "The Essential Guide to Image Processing. "Academic Press, pp 324-348, July, 2009
  6. Gonzalez, R. , Woods, R. , and Eddins, S. , "Digital Image Processing Using MATLAB. " Pearson Prentice-Hall, Upper Saddle River, NJ, 2004.
  7. H. W. Engl, M. Hanke, and A. Neubauer, "Regularization of Inverse Problems. " Kluwer Academic Publishers, pp. 127–150, 2000.
  8. C. W. Groetsch, "The Theory of Tikhonov Regularization for Fredholm Equations of the First Kind". London: Pitman, 1984.
  9. S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, "An iterative regularization method for total variation-based image restoration," SIAM Multiscale Model. Sim. , vol. 4, pp. 460–489, 2005.
  10. M. Elad, "On the origin of the bilateral filter and ways to improve it,"IEEE Trans. Image Process. , vol. 11, no. 10, pp. 1141–1151, Oct. 2002.
  11. M. Elad, "Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. " Berlin: Springer, 2010.
  12. J. Myrheim and H. Rue, "New algorithms for maximum entropy image restoration," CVGIP: Graphical Models and Image Processing, vol. 54, no. 3, pp. 223–238, May 1992.
  13. P. C. Hansen, "Analysis of discrete ill-posed problems by means of the L-curve," SIAM Review, vol. 34, no. 4, pp. 561–580, December 1992.
  14. A. K. Katsaggelos and S. N. Efstratiadis. , "A class of iterative signal restoration algorithms. " IEEE Trans. Acoust. , Vol. 38, pp. 778–786, 1990
  15. L. Landweber, "An iteration formula for Fredholm integral equations of the first kind,"American Journal of Mathematics, vol. 73, no. 3, pp. 615–624, July 1951.
  16. D. G. Luenberger and Y. Ye, "Linear and Nonlinear Programming. Berlin: Springer, 2008
  17. W. H. Richardson, "Bayesian-based iterative method of image restoration," Journal of the Optical Society of America, vol. 62, no1, pp. 55–59, January 1972.
  18. L. B. Lucy, "An iteration technique for the rectification of the obscured distributions," The Astronomical Journal, vol. 79, no. 6, pp. 745–754, June 1974.
  19. H. Stark and P. Oskoui, "High-resolution image recovery from image-plane arrays using convex projections," Journal of the Optical Society of America A, vol. 6, no. 11,pp. 1715–1726, November 1989.
  20. A. M. Tekalp, M. K. Ozkan, and M. I. Sezan, "High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration," in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, March 1992, pp. 169–172
  21. C. B. Atkins, C. A. Bouman, and J. P. Allebach, "Optimal image scaling using pixel classification," Proceedings of the IEEE International Conference on Image Processing, vol. 3, pp. 864–867, October 2001.
  22. D. Capel and A. Zisserman, "Computer vision applied to super resolution," IEEE Signal Processing Magazine, vol. 20, no. 3, pp. 75–86, May 2003.
  23. Liyakathunisa, and V. K. Ananthashayana, "Super Resolution Blind Reconstruction of Low Resolution Images using Wavelets based Fusion", World Academy of Science, Engineering and Technology 40, pp 177-181, 2008.
  24. R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman, "Removing camera shake from a single photograph," ACM Transactions on Graphics, vol. 25, no. 3, pp. 787–794, July 2006
  25. S. Osher and L. I. Rudin, "Feature-oriented image enhancement using shock filters. " SIAM Journal on Numerical Analysis, vol. 27, no. 4, pp. 919–940, August 1990.
  26. J. W. Miskin and D. J. C. MacKay, "Ensemble learning for blind image separation and deconvolution," Advances in Independent Component Analysis. pp. 123–141, July 2000
  27. Ayers G. R. , and Dainty J. C. , "Iterative blind deconvolution methods and its applications," Optics Letter, vol. 13, no. 7, July 1988.
  28. Lajendijk, R. L. , Biemond J. , and Boekee, D. E. , "Regularized Iterative Image Restoration with Ringing Reduction. IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 36, no. 12, 1874-1888, 1988
  29. Kundur D. and Hatzinakos D. , "A novel blind deconvolution scheme for image restoration using recursive filtering," IEEE Transactions on Signal Processing, vol. 46, no. 2, pp. 375-390, 1998
  30. I. F. Gorodnitsky and B. D. Rao, "Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm," IEEE Transactions on Image Processing, vol. 45, no. 3, pp. 600–616, March 1997.
  31. J. M. Bioucas-Dias, M. A. T. Figueiredo, and J. P. Oliveira, "Total variation-based image deconvolution: A majorization-minimization approach," Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 861–864, May 2006.
  32. J. M. Bioucas-Dias and M. A. T. Figueiredo, "A new twIST: Two-step iterative shrinkage/ thresholding algorithms for image restoration," IEEE Transactions on Image Processing, vol. 16, no. 12, pp. 2992–3004, December 2007
  33. G. Aubert and P. Kornprobst, "Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations. Berlin: Springer, 2006.
  34. I. Markovsky and S. Van Huffel, "Overview of total least-squares methods," Signal Processing, vol. 87, no. 10, pp. 2283–2302, October 2007.
  35. D. S. G. Pollock, "A Handbook of Time-Series Analysis, Signal Processing and Dynamics. New York: Academic Press, 1999.
  36. W. Dong, L. Zhang, G. Shi and X. Wu, "Image deblurring and supper-resolution by adaptive sparse domain selection and adaptive regularization," in IEEE Trans. on Image Processing, vol. 20, no. 8, pp. 2378-2386, 2011.
  37. Moacir P. Ponti, Nelson D. , A. Mascarenhas, Paulo J. , S. G. Ferreira, and Claudio A. T. Suazo, "Three-dimensional noisy image restoration using filtered extrapolation and deconvolution,"springer, 2011
  38. Amir Beck and Marc Teboulle, "A Fast Iterative Shrinkage Thresholding Algorithm for Linear Inverse Problems", SIAM Imageing Sciences, Vol. 2, No. 1, pp. 183-202, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Blur Spatial-Invariance PSF Deconvolution