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Reseach Article

A Review on Digital Image Restoration Process

by Sujita Pillai, Sanjay Khadagade
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 158 - Number 7
Year of Publication: 2017
Authors: Sujita Pillai, Sanjay Khadagade
10.5120/ijca2017912862

Sujita Pillai, Sanjay Khadagade . A Review on Digital Image Restoration Process. International Journal of Computer Applications. 158, 7 ( Jan 2017), 40-42. DOI=10.5120/ijca2017912862

@article{ 10.5120/ijca2017912862,
author = { Sujita Pillai, Sanjay Khadagade },
title = { A Review on Digital Image Restoration Process },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2017 },
volume = { 158 },
number = { 7 },
month = { Jan },
year = { 2017 },
issn = { 0975-8887 },
pages = { 40-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume158/number7/26924-2017912862/ },
doi = { 10.5120/ijca2017912862 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:04:15.189317+05:30
%A Sujita Pillai
%A Sanjay Khadagade
%T A Review on Digital Image Restoration Process
%J International Journal of Computer Applications
%@ 0975-8887
%V 158
%N 7
%P 40-42
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image restoration is the process of restoring degraded images which cannot be taken again or the process of obtaining the image again is costlier. We can restore the images by prior knowledge of the noise or the disturbance that causes the degradation in the image. Image restoration is done in two domains: spatial domain and frequency domain .image can provide an insight for filtering operations. After the filtering, the image is remapped into spatial domain by inverse Fourier transform to obtain the restored image. Restoration efficiency was checked by taking signal to noise ratio (snr) and mean square error(mse) into considerations..

References
  1. Image Restoration From a Single Blurred Photograph Li Yang School of Arts and Communications, Anhui University Hefei 230011, China 2016 3rd International Conference on Information Science and Control Engineering
  2. Fast Weighted Total Variation Regularization Algorithm for Blur Identification and Image Restoration Haiying Liu Member, IEEE, Jason Gu Senior Member, IEEE, Max Q.-H. Meng, Fellow, IEEE Wu-Sheng Lu Life Fellow, IEEE, School of Electrical Engineering and Automation, Qilu University of Technology, Jinan, 250353, China
  3. fast and restoration of blur images based on the local patches tomi goto department of computer science and engineering japan nayoga institute of technology
  4. Linear Blur Compensation in Digital Images Using Lucy-Richardson MethodK. Panfilova#1, S. Umnyashkin#2 Dept. of Higher mathematics National Research University of Electronic Technology Moscow, Zelenograd, Russia
  5. Image Deblurring Using a Pyramid-Based Richardson–Lucy Algorithm Jian-Jiun Ding1, Wei-De Chang2, Yu Chen3, Szu-Wei Fu4 Graduate Institution of Communication Engineering National Taiwan University Taipei, Taiwan
  6. David L. Donoho, “De-noising by soft-thresholding,Dept of Statistics, Stanford University, 1992
  7. Hiroko Furuya and Shintaro Eda, “Image Restoration via Wiener Filtering in the requency Domain”,
  8. Hui Li, B.S. Manjunath, Sanjit K. Mitra H. Li, B. S. Manjunath and S. K. Mitra, Multisensor Image Fusion Using the Wavelet Transform, Proc. first International conference on image processing, ICIP 94, Austin, Texas, Vol. I, Pages 51-55, Nov 1994.
  9. Investigations of Image Fusion, Lehigh University.
  10. Jain Anil K.,”Fundamentals of Digital Image Processing”, Davis:Prentice-Hall ofIndia, 2000.
  11. Kundur, D. and D. Hatzinakos, “Blind Image Deconvolution”, IEEE SignalProcessing Magazine, vol. 13 (3), pp. 43-64, May 1996.
  12. Kaur. A. Chopra. V, “A Comparative Study and Analysis of Image Restoration Techniques Using Different Images Formats”, “International Journal for Science and Emerging Technologies with Latest Trends” 2(1): 7-14 (2012).
  13. L.Prasad and S. S. Iyengar, Wavelet Analysis with Applications to Image Processing. Boca Raton, FL: CRC Press LLC, 1997, pp.101-115.
  14. Mandelbrot, B., and Wallis, J., "Noah, Joseph and operational hydrology," Water Resources Research 4, 909-918, 1968.
  15. M.A.Joshi, Digital Image Processing – An Algorithmic Approach, Professors and Head, Department of Telecommunications, College of Engineering, Pune,Prentice Hall of India Private Limited, New Delhi, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Restoration De-blur De-convolution Filtering Noise.