Image Restoration Approaches for Image Quality Improvement

Authors

  • Deep Kumar  Software Engineer, Igniva Solutions Private Limited, Mohali, Punjab, India

Keywords:

FFT, Inverse Filter, RGB, YCBCR, and CMYK

Abstract

Image restoration is the process of image quality enhancement so that noise and blurriness from the image can be removed. In the process of image restoration image has been subdivided into several parts so that image parts can be used for enhancement process using various image enhancement approaches and filters. After implementation of filters all the parts have been recombined so that image quality can be enhanced. In this paper various approaches that has been used for image restoration process has been reviewed. On the basis of these approaches quality of the image has been improved and used for further applications.

References

  1. Nicolaos B. Karayiannis "Regularization Theory in Image Restoration-The Stabilizing Functional Approach", IEEE Transactions On Acoustics. Speech, And Signal Processing. Vol 38. No 7. July 1990.
  2. Gajanand Gupta "Algorithm for Image Processing Using Improved Median Filter and Comparison of Mean, Median and Improved Median Filter", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-1, Issue-5, November 2011.
  3. AnamikaMaurya, RajinderTiwari "A Novel Method of Image Restoration by using Different Types of Filtering Techniques", International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 3, Issue 4, July 2014..
  4. Herng-Hua Chang "Brain MR Image Restoration Using an Automatic Trilateral Filter With GPU-Based Acceleration", IEEE Transactions on Biomedical Engineering, IEEE Transactions on Biomedical Engineering (Volume: 65, Issue: 2, Feb. 2018).
  5. Jos Bratti;Joel Gaya "Understading Image Restoration Convolutional Neural Networks with Network Inversion" , Machine Learning and Applications (ICMLA), 2017.
  6. XiaoyanXu, "Image Denoising in Wavelet Domain", School of Engineering, University of Guelph,E-mail: xux@uoguelph. Carl Taswell , " Experiments in
  7. Wavelet Shrinkage Denoising"C. Taswell ([email protected]), Computational Toolsmiths, POB 18925, Stanford, CA 94309-8925.
  8. Yi Wan and Robert D. Nowak ," Wavelet-Based Statistical Model For Image Restoration " , Rice University ,Department of Electrical Engineering,
  9. 6100 South Main Street, Houston, TX 77005, USA.
  10. Javier Portilla and Eero P. Simoncelli ," Image Denoising Via Adjustment of Wavelete Coefficient Magnitude Correlation", Center for Neural Science, and Courant Institute of Mathematical Sciences New York University, NY 10003, E-mail: fjavier,[email protected].
  11. Jae S. Lim, " Image Restoration by Short Space Spectral Subtraction", IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-28, No. 2, April, 1980.
  12. A. Buades, B. Coll, And J. M. Morel, "A review of Image Denoising Algorithms with a New One", Multiscale Model. Simul. 2005 Society for Industrial and Applied Mathematics, Vol. 4, No. 2, pp. 490-530.

Downloads

Published

2017-04-30

Issue

Section

Research Articles

How to Cite

[1]
Deep Kumar, " Image Restoration Approaches for Image Quality Improvement, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 2, pp.1164-1168, March-April-2017.