Open Access Open Access  Restricted Access Subscription or Fee Access

Performance Analysis and Automatic Selection of Restoration Techniques for Diversified Field Images

Shraddha K. Hatwar, A. L. Wanare, Dr. Dilip D. Shah, Dr. J. B. Helonde

Abstract


In this Paper, a new formulation based on Least Square Regression (LSR) is discussed for image alignment. Contrary to the conventional approach, LSR technique takes an advantage in image restoration. Based on this formulation, three methods Wiener filter, Regularized filter and Blind Deconvolution are proposed and Peak Signal to Noise Ratio (PSNR) is discussed. Analysis is carried out by comparing the combination of single type of image and noise dealing PSNR for each image. We proposed automatic parameter estimation and selection of restoration methods for diversified field images.

Keywords


Blind Deconvolution, Least Square Regression, Regularized Filter, Wiener Filter.

Full Text:

PDF

References


Digital Image Processing By Gonzalez Woods and Eddins.

Digital Image Processing Using MATLAB by Gonzalez Woods and Eddins.

Image processing and data analysis by Fionn Murtagh University of Ulster Albert Bijaoui Observatoire de la Cˆote d‟Azur.

Mr. Salem Saleh Al-amri, Dr. N.V. Kalyankar, “A Comparative Study for Deblured Average Blurred Images”, International Journal on Computer Science and Engineering Vol. 02, No. 03, 2010, 731-733

Er.Neha Gulati, Er.Ajay Kaushik, “Remote Sensing Image Restoration Using Various Techniques: A Review”, International Journal of Scientific & Engineering Research, Volume 3, Issue 1, January-2012.

Charu Khare, Kapil Kumar Nagwanshi, “Implementation and Analysis of Image Restoration Techniques”, International Journal of Computer Trends and Technology- May to June Issue 2011

Ratnakar Dash, “Parameters Estimation For Image Restoration”, Dissertation report of Doctor of Philosophy

M. Sindhant Devi, V. Radhika “Comparative approach for speckle reduction in medical images,” International Journal of ART, Vol.01, Issue 01, pp-7-11, 2011.

Mr. Anil L. Wanare, Dr.Dilip D. Shah, “Performance Analysis and Optimization of Nonlinear Image Restoration Techniques in Spatial Domain”, International Journal of Image Processing (IJIP), Volume (6) : Issue (2) : 2012

Deepa Kundur, Dimitrios Hatzinakos, “A Novel Blind Deconvolution Scheme For Image Restoration Using Recursive Filtering”, IEEE Transactions On Signal Processing, Vol. 46, No. 2, February 1998

Xiaoyang Yu, Yuan Gao, Xue yang, Chu Shi, Xiukun Yang, “Image Restoration Method Based on Least Squares and Regularization and Fourth Order Partial Differential Equation” , Information Technology Journal 9(5): 962-967, 2010.

Carl W. Helstrom, “Image Restoration By The Method Of Least Squares”, Journal Of The Optical Society Of America Volume 57, Number 3 March 1967.

Nikolas P. Galatsanos, Aggelos K. Kasaggelos, Ronald D. Chin, “Least Square Restoration of Multichannel Images, IEEE Transaction on Signal Processing, Vol 39, No. 10. October 1991.

A. Khireddine, K. Benmahammed, W. Puech, “Digital image restoration by Wiener filter in 2D case”, Advances in Engineering Software 38 (2007) 513–516

Nicol`o Cesa-Bianchi, “Applications of regularized least squares to pattern classification”, Theoretical Computer Science 382 (2007) 221–231

Punam A. Patil, Prof.R.B.Wagh, “Review of blind image restoration methods”, World Journal of Science and Technology 2012, 2(3):168-170

Andr´es G. Marrugo, Michal ˇ Sorel, Filip ˇSroubek, Mar´ıa S. Mill´an, “Retinal image restoration by means of blind deconvolution”, Journal of Biomedical Optics 16(11), 116016 (November 2011)

N.F. Law, R.G. Lane, “Blind Deconvolution Using Least Squares Minimization”, Optics Communications 128 (1996) 341-352

D. Martin, C. Fowlkes, D. Tal and J. Malik, “A database of natural images and its applications to evaluating segmentation algorithms and measuring ecological statestics”, proc. IEEE conf. computer vision volume , pp 416-423, July 2001.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.