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Image Restoration Techniques: A Survey

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Monika Maru, M. C. Parikh
10.5120/ijca2017913060

Monika Maru and M C Parikh. Image Restoration Techniques: A Survey. International Journal of Computer Applications 160(6):15-19, February 2017. BibTeX

@article{10.5120/ijca2017913060,
	author = {Monika Maru and M. C. Parikh},
	title = {Image Restoration Techniques: A Survey},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2017},
	volume = {160},
	number = {6},
	month = {Feb},
	year = {2017},
	issn = {0975-8887},
	pages = {15-19},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume160/number6/27077-2017913060},
	doi = {10.5120/ijca2017913060},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

During the process of image acquisition, sometimes images are degraded by various reasons. Image restoration is a challenging task in the field of Image processing. The process of recovering such degraded or corrupted image is called Image Restoration. Restoration process improves the appearance of the image. The degraded image is the convolution of the original image, degraded function, and additive noise. The process of restoration is deconvolved this degraded image to obtain noiselessly and deblurred original image. Various methods available for image restoration such as inverse filter, Weiner filter, constrained least square filter, blind deconvolution method etc. some of the methods are either linear or non-linear method helps to remove noise and blur from the image. In this description and comparison of restoration techniques are mentioned. In this paper, various spatial domain filters are discussed which are used to remove noise from the images.

References

  1. B. Xiao-Jun and W. Ting, “Adaptive blind image restoration algorithm of degraded image,” Proc. - 1st Int. Congr. Image Signal Process. CISP 2008, vol. 3, pp. 536–540, 2008.
  2. T. Singh, “Novel Restoration Process for Degraded Image,” in 2014 Fifth International Conference on Signal and Image Processing, 2014, pp. 81–86.
  3. R. Kaur and E. N. Singh, “Image Restoration - A Survey,” IOSR J. Comput. Eng., vol. 16, no. 4, pp. 107–111, 2014.
  4. S. Kumar, “Performance Evaluation and Analysis of Image Restoration Technique using DWT,” vol. 72, no. 18, pp. 11–20, 2013.
  5. C. Liu, Y. Zhang, H. Wang, and X. Wang, “Improved block Kalman filter for degraded image restoration,” Proc. - 2013 IEEE Int. Conf. High Perform. Comput. Commun. HPCC 2013 2013 IEEE Int. Conf. Embed. Ubiquitous Comput. EUC 2013, pp. 1958–1962, 2014.
  6. D. Yang and S. Qin, “Restoration of degraded image with partial blurred regions based on blur detection and classification,” in 2015 IEEE International Conference on Mechatronics and Automation (ICMA), 2015, pp. 2414–2419.
  7. A. Thakur and A. Iqbae, “Comparison efficacy of restoration method for Space Variant Motion Blurred Images using kalman & wiener filter,” 2016.
  8. W. F. Al Maki, T. Shimahashi, T. Kitagawa, and S. Sugimoto, “Space invariant blur estimation and noiseless kalman filter-based image deconvolution,” Int. J. Innov. Comput. Inf. Control, vol. 5, no. 1, pp. 201–213, 2009.
  9. M. Aggarwal, R. Kaur, and B. Kaur, “A Review of Denoising Filters in Image Restoration,” Int. J. Curr. Res. Acad. Rev., vol. 2, no. 3, pp. 83–89, 2014.
  10. B. Su and S. Lu, “Blurred Image Region Detection and Classification.”
  11. A. Maurya and R. Tiwari, “A Novel Method of Image Restoration by using Different Types of Filtering Techniques,” Int. J. Eng. Sci. Innov. Technol., vol. 3, no. 4, pp. 124–129, 2014.
  12. D. Broutman, J. W. Rottman, and S. D. Eckermann, “R Ay M Ethods for I Nternal W Aves in the,” pp. 1–25, 2004.
  13. P. Dhole and N. Chopde, “A Comparative Approach for Analysis of Image Restoration using Image Deblurring Techniques,” Int. J. Curr. Eng. Technol., vol. 5, no. 2, pp. 1046–1049, 2015.
  14. R. Nagayasu, N. Hosoda, N. Tanabe, H. Matsue, and T. Furukawa, “Restoration method for degraded images using two-dimensional block Kalman filter with colored driving source,” 2011 Digit. Signal Process. Signal Process. Educ. Meet. DSP/SPE 2011 - Proc., pp. 151–156, 2011.
  15. A. Thakur, A. Kausar, and A. Iqbal, “Comparison efficacy of restoration method for space variant motion blurred images using kalman & wiener filter,” in 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), 2016, pp. 508–512.
  16. P. D. Samarasinghe and R. A. Kennedy, “On Non-blind Image Restoration,” 2009.
  17. Chidananda Murthy M V, M. Z. Kurian, and H. S. Guruprasad, “Performance evaluation of image restoration methods for comparative analysis with and without noise,” in 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), 2015, pp. 282–287.
  18. Y. Wang, “Image filtering: noise removal, sharpening, deblurring,” http//eeweb.poly.edu/~yao Polytech., p. 41, 2006.
  19. L. Yan, M. Jin, H. Fang, H. Liu, and T. Zhang, “Atmospheric-turbulence-degraded astronomical image restoration by minimizing second-order central moment,” IEEE Geosci. Remote Sens. Lett., vol. 9, no. 4, pp. 672–676, 2012.

Keywords

Image Restoration, Degraded Image, Blur, Noise, PSF