Inverse Techniques for Efficient Corneal Image Restoration | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 13, Volume 29, Issue 2, July 2020, Page 70-74 PDF (987.25 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2020.103954 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Abd El-Rahman Farouk1; H.I. Ashiba2; G.M. Elbanby3; A.S. El-Fishawy1; M. I. Dessouky1; E.M. El- Rabaie1; F.E. Abd El-Samie1 | ||||
| 1Department of Electronics and Electrical Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt | ||||
| 2Department of Electronics and Electrical Communications, Bilbis higher institute of Engineering, Bilbis, sharqia , Egypt | ||||
| 3Department of Automatic Control, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt | ||||
| Abstract | ||||
| This paper presents two proposed approaches for digital restoration of corneal images. The first algorithm is based on Wiener Restoration approach. The second algorithm depends on regularized image restoration. As corneal images are usually acquired with confocal microscopes. Hence if the corneal layer is outside the focus of the microscopes, the image will be blurred. To solve this problem, the restoration process can be applied on the corneal image. Both Linear Minimum Mean Square Error (LMMSE) and regularized restoration are implemented. The evaluation metrics used to test the performance of the proposed restoration approaches are mean square error (MSE), peak signal to noise ratio (PSNR) and correlation coefficient. Simulations results reveal good success in restoration of corneal images refer to the mentioned evaluation metrics and appearance view. | ||||
| References | ||||
|
[1] Ishfaq Bashir et al, “Image Restoration and the Various Restoration Techniques Used in the Field of Digital Image Processing,” International Journal of Computer Science and Mobile Computing, Vol.6, NO. 6, pg. 390-393, June- 2017. [2] H.C. Anderws and B.R. Hunt, “Digital Image Restoration” Englewood Cliffs, NJ: Prentice- Hall, 1977 [3] M .S. Maheshan , B. S. Harish and N. Nagadarshan,"On The Use of Image Enhancement Technique Towards Robust Sclera Segmentation, " Procedia Computer Science 143 , 466– 473,2018. [4] Monika Maru and M C Parikh, “Image Restoration Techniques: A Survey,” International Journal of Computer Applications, Vol. 160, No.6, pp.15-19, February 2017. [5] H. Riyaz Fathima, K. Madhan Kumar, “Evaluation of Blind image Restoration,” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 3, NO. 12, pp.4210-4215, December 2014. [6] Nicolas B. Karayiannis and Anastasios N. Venetsanopoulos, “Regularization Theory In Image Restoration- The Stabilizing Functional Approach,” IEEE Trans. Acoustics, Speech and Signal Processing, Vol. 38 , No.7, pp.1155-1179, July 1990. [7] S. Kumar, “Performance Evaluation and Analysis of Image Restoration Technique using DWT,” International Journal of Computer Applications, Vol. 72, no.18, pp. 11–20, 2013 [8] Andereas E. Savakis and H. Joel Trussell, “ Blur Identification By Residual Spectral Matching,” IEEE Trans. Image Processing , Vol. 2, No.2, pp.141-150, April 1993 [9] GordanaPavalović and Murat Tekalp, “Maximum Likelihood Parametric Blur Identification Based On A Continious Spatial Domain Model,” IEEE Trans. Image Processing, Vol. 1, No.4, pp. 496-504, Oct. 1992. [10] A. Antoniadis, “Wavelet methods in statistics: Some recent developments and their applications,” Statistics Surveys, Vol. 1, pp.16–55, 2007. [11] Reginald L. Lagendijk, Jan Biemond and Dick E. Boekee, “Regularized Iterative Image Restoration With Ringing Reduction,” IEEE Trans. Acoustics, Speech and Signal Processing, Vol. 36, No.12. , pp 1874-1888, Dec 1988. [12] Nikolas. P. Galatsanos, Vladimir Z. Mesarović, Rafael Molina and Aggelos K. Katasaggelos, “ Hierarchical Bayesian Image Restoration From Partially Known Blurs,” IEEE Trans. Image Processing , Vol. 9, No.10, pp.1784-1797, Oct. 2000. | ||||
|
Statistics Article View: 137 PDF Download: 182 |
||||
