Image Restoration Based on Deconvolution by Richardson Lucy Algorithm
Citation
Madri Thakur , Shilpa Datar. "Image Restoration Based on Deconvolution by Richardson Lucy Algorithm", International Journal of Engineering Trends and Technology (IJETT), V14(4),161-165 Aug 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
This article presents the performance analysis of different basic techniques used for the image restoration. Restoration is a process by which an image suffering from degradation can be recovered to its original form. Removing blur and noise from image is very difficult problem to solve. We have implemented the three different techniques of image restoration and tested our implementation for the blurred image in the standard environment. We have obtained the blurred image with the standard blurring functions and the noise. The degraded images have been restored by the use of Wiener deconvolution, Inverse deconvolution and Richardson–Lucy algorithm. Further we have compared the different results on the basis of PSNR and MSE values of the restored image. Finally the conclusion is formulated.
References
[1] J. G. Walker, D. A. Fish, A. M. Brinicombe, and E. R. Pike, “Blind deconvolution by means of the Richardson–Lucy algorithm” J. Opt. Soc. Am. A, Vol. 12, No. 1, January 1995.
[2] Arijit Dutta, Aurindam Dhar, Kaustav Nandy, Project report on “Image Deconvolution By Richardson Lucy Algorithm”, Indian Statistical Institute, November, 2010.
[3] G. R. Ayers and J. C. Dainty, “Iterative blind deconvolution method and its applications,” Opt. Lett. Vol.13, 547–549 , 1988 .
[4] J. R. Feinup, “Phase retrieval algorithms: a comparison,” Appl. Opt. Vol.21, 2758–2769, 1982.
[5] B. L. K. Davey, R. G. Lane, and R. H. T. Bates, “Blind deconvolution of noisy complex-valued image,” Opt. Commun. Vol.69, 353–356, 1989.
[6] W. H. Richardson, “Bayesian-based iterative method of image restoration,” J. Opt. Soc. Am. Vol.62, 55–59 1972.
[7] L. B. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J. Vol. 79, 745–754, 1974.
[8] Ramesh Neelamani, Thesis report on “Inverse problems in image processing” Electrical and Computer Engineering Rice University, Houston, Texas.
[9] Reginald L. Lagendijk and Jan Biemond, “Basic methods for image restoration and Identification”, Lagendijk- Biemond, February, 1999
Keywords
Inverse filter, Wiener filter, Lucy- Richardson, MSE (mean square error), PSNR (peak signal to noise ratio).