Motion blur caused by relative motion between the camera and the object being captured is an everyday situation that deteriorates the quality of the images largely. Even a photograph captured in low light conditions or that of a fast moving object undergo motion blur and cause significant degradation of the image and demands for deblurring the same to reconstruct the original image. The paper addresses this commonly encountered problem and carries out a thorough experimental investigation of several non-blind and blind motion deblurring algorithms. Both qualitative and quantitative assessment based on popular performance metrics viz., peak signal-to-noise ratio (PSNR) and mean squared error (MSE) is performed. Through this comparative analysis the properties and limitations of these deblurring algorithms are explored and verified.

1. Banham, M. R. and Katsaggelos, A. K. Digital image restoration. IEEE signal processing magazine, 24-41, 1997.
2. Ben-Ezra, M. and Nayar, S. K. Motion-based motion deblurring. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 6, 689-698, 2004.
3. Bertero, M. and Boccacci, P. A simple method for the reduction of boundary effects in the Richardson-Lucy approach to image deconvolution. Astronomy and Astrophysics, 369-374, 2005.
4. Chalmond, B. 1991. PSF estimation for image deblurring. CVGIP: Graphical Models and Image Processing, vol. 53, no. 4, 364-372.
5. Gonzalez, R., Woods, R., and Eddins, S. Digital Image Processing Using MATLAB. Pearson Prentice-Hall, Upper Saddle River, NJ, 2004.
6. Hunt, B. The application of constrained least squares estimation to image restoration by digital computer. IEEE Transactions on Computer, vol. 2, 805-812, 1973.
7. Lucy, L. An iterative technique for the rectification of observed distributions. The Astronomical Journal, vol. 79, no. 6, 745-754, 1974.
8. Mesarovic, V. Z., Galatsanos, N. P., and Katsaggelos, A. K. Regularized constrained total least squares image restoration. IEEE Transactions on Signal Processing, vol. 4, no. 8, 1096-1108, 1995.
9. Moghaddam, M. E., and Jamzad, M. Motion blur identification in noisy images using mathematical models and statistical measures. Pattern Recognition, vol. 40, 1946-1957, 2007.
10. Richardson, W. H. Bayesian-based iterative method of image restoration. Journal of the Optical Society of America, vol. 62, no. 1, 55-59, 1972.
11. Jiang, X., Cheng, D. C., Wachenfeld, S., Rothaus, K. Motion Deblurring. University of Muenster, Department of Mathematics and Computer Science, 2005.
12. Ayers G. R., and Dainty J. C. Iterative blind deconvolution methods and its applications. Optics Letter, vol. 13, no. 7, July 1988.
13. 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.
14. 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.
15. Raskar R., Agrawal A., and Tumblin J. Coded exposure photography: motion deblurring using fluttered shutter, ACM Transactions on Graphics (TOG), vol. 25, no. 3, 795–804, 2006.
16. Biemond, J., Lagendijk, R., and Mersereau, R. M. Iterative methods for image deblurring," in Proceedings of the IEEE, vol. 78, no. 5, pp. 856-883, 1990.
17. Shao-jie, S., Qiong, W., and Guo-hui, L. Blind image deconvolution for single motion-blurred image, The Journal of China Universities of Posts and Telecommunications, vol. 17, no. 3, pp. 104-109, 2010.
18. Mariana, S. C., and Almeida, L. B. Blind and semi-blind deblurring of natural images, IEEE Transactions on Image Processing, vol. 19, no. 1, pp. 36-52, 2010.

Deblurring Deconvolution Motion blur Point Spread function Gaussian noise