CFP last date
22 April 2024
Reseach Article

A Comparative Study to Noise Models and Image Restoration Techniques

by Prabhishek Singh, Raj Shree
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 149 - Number 1
Year of Publication: 2016
Authors: Prabhishek Singh, Raj Shree
10.5120/ijca2016911336

Prabhishek Singh, Raj Shree . A Comparative Study to Noise Models and Image Restoration Techniques. International Journal of Computer Applications. 149, 1 ( Sep 2016), 18-27. DOI=10.5120/ijca2016911336

@article{ 10.5120/ijca2016911336,
author = { Prabhishek Singh, Raj Shree },
title = { A Comparative Study to Noise Models and Image Restoration Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 149 },
number = { 1 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 18-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume149/number1/25960-2016911336/ },
doi = { 10.5120/ijca2016911336 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:53:31.230170+05:30
%A Prabhishek Singh
%A Raj Shree
%T A Comparative Study to Noise Models and Image Restoration Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 149
%N 1
%P 18-27
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image Restoration is one of area related to image processing which deals with restoring an original and sharp image from corrupted image using a mathematical degradation and restoration model. In this proposed work, a comparative study analysis of simple, fast technique is given to remove noise of an image which is mostly introduced due to environmental changes or due to other issues. Researchers focus on the noise issues that changes image pixels value either on or off. To get an enough efficient method to remove the noise from the images is a greater challenge for the researchers. Noise plays an important role in degrading the image at the time of capturing or transmission of the image. There are many algorithms and filtering techniques available which have their own assumptions, merits and demerits depending upon the prior knowledge of the noise. Image smoothening is one of the most significant and widely used procedure in the image processing. Here, apart from noise a model, the light is also thrown on comparative analysis of noise removal techniques is done. This paper will present the different noise types to an image models and investigating the various noise reduction techniques and their advantages and disadvantages and also it will help the new researchers to have the detailed and comparative knowledge regarding image restoration and all its associated details.

References
  1. Dhananjay K. Theckedath, Digital Image Processing (Using MATLAB Codes), Second Revised Edition: July 2013.
  2. Ruchika Chandel, Gaurav Gupta, Image Filtering Algorithms and Techniques: A Review, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, October 2013
  3. Kailas, T.,”A view of three decades of linear filtering theory”, Stanford University, Stanford, CA, USA.
  4. Datum. F,” Aerospace and Electronic Systems Magazine”, IEEE, 2005.
  5. http://www.journals.elsevier.com/signal-processing/call-for-papers/special-issue-on-image-restoration/
  6. Lexing Xie, Image Restoration, Lecture 7, March 23rd 2009, EE4830 Digital Image Processing, http://www.ee.columbia.edu/~xlx/ee4830/
  7. ”Digital Image Processing” R. C. Gonzalez and R. E. Woods, 2nd Ed., Englewood Cliffs, Nj: Prentice Hall, 2002.
  8. Pragati Agrawal, Jayendra Singh Verma , A Survey of Linear and Non-Linear Filters for Noise Reduction, International Journal of Advance Research in Computer Science and Management Studies, Volume 1, Issue 3, August 2013.
  9. Rinku Kalotra , Sh. Anil Sagar, A Review: A Novel Algorithm for Blurred Image Restoration in the field of Medical Imaging, International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 6, June 2014.
  10. Anamika Maurya, Rajinder Tiwari, 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.
  11. Amandeep Kaur Vinay Chopra, “Blind Image Deconvolution Technique for Image Restoration Using Ant Colony Optimization,” International Journal of Computer Applications & Information Technology Vol. I, Issue II, September 2012.
  12. Amandeep Kaur , Vinay Chopra, A Comparative Study and Analysis of Image Restoration
  13. Techniques Using Different Images Formats, “International Journal for Science and Emerging Technologies with Latest Trends” 2(1): 7-14 (2012).
  14. Rohit Verma, Dr. Jahid Ali, A Comparative Study of Various Types of Image Noise and Efficient Noise Removal Techniques, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, October 2013.
  15. Dougherty G. (2010) “Digital Image Processing for Medical Applications,” second ed., Cambridge university press.
  16. Ajay Kumar Boyat, Brijendra Kumar Joshi, A REVIEW PAPER: NOISE MODELS IN DIGITAL IMAGE PROCESSING, Signal & Image Processing: An International Journal (SIPIJ) Vol.6, No.2, April 2015.
  17. Boyat, A. and Joshi, B. K. (2013) “Image Denoising using Wavelet Transform and Median Filtering’, IEEE Nirma University International Conference on Engineering,” Ahemdabad.
  18. Kamboj P. & Rani V., (2013) “A Brief study of various noise models and filtering techniques,” Journal of Global Research in Computer Science, Vol. 4, No. 4.
  19. Catipovic M. A., Tyler P. M., Trapani J. G., & Carter A. R., (2013) “Improving the quantification of Brownian motion,” American Journal of Physics, Vol.81 No. 7 pp. 485-491.
  20. Bhattacharya J. K., Chakraborty D., & Samanta H. S., (2005) “Brownian Motion - Past and Present,” Cornall university library. arXiv:cond-mat/0511389
  21. Radenovic A., “Brownian motion and single particle tracking,” Advanced Bioengineering methods laboratory, Ecole polyteachenique federal de Lausanne.
  22. Peidle J., Stokes C., Hart R., Franklin M., Newburgh R., Pahk J., Rueckner W. & Samuel AD, (2009) “Inexpensive microscopy for introductory laboratorycourses,” American Journal of Physics Vol. 77 pp. 931-938.
  23. Nakroshis P., Amoroso M., Legere J. & Smith C., (2003) “Measuring Boltzmann’s constant using video microscopy of Brownian motion,” American Journal of Physics, Vol. 71, No. 6, pp. 568-573.
  24. Radenovic A., “Brownian motion and single particle tracking,” Advanced Bioengineering methods laboratory, Ecole polyteachenique federal de Lausanne.
  25. Astola J. & Kuosmanen P. (1997) “Fundamentals of nonlinear digital filtering,” CRC Press, Boca Raton.
  26. Chabay R. W., & Sherwood B. A., (2009) “Matter and Interactions,” 3rd edition, John Wiley and Sons.
  27. Joshi, A., Boyat, A. and Joshi, B. K. (2014) “Impact of Wavelet Transform and Median Filtering on removal of Salt and Pepper noise in Digital Images,” IEEE International Conference on Issues and Challenges in Intelligant Computing Teachniques, Gaziabad.
  28. Hosseini H. & Marvasti F., (2013) “Fast restoration of natural images corrupted by high-density impulse noise,” EURASIP Journal on Image and Video Processing. doi:10.1186/1687-5281-2013-15
  29. Koli M. & Balaji S., (2013) “Literature survey on impulse noise reduction,” Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.5.
  30. Zhang L., Dong W., Zhang D. & Shi G. (2010) “Two stage denoising by principal component analysis with local pixel grouping,” Elsevier Pattern Recognition, Vol. 43, Issue 4, pp. 1531-1549.
  31. Behrens R. T. (1990) “Subspace signal processing in structured noise,” Thesis, Faculty of the Graduate School of the University of Colorado, the degree of Doctor of Philosophy, Department of Electrical and Computer Engineering.
  32. Schowengerdt R. A. (1983) “Techniques for Image Processing and classifications in Remote Sensing,” First Edition Academic Press.
  33. Kamboj P. & Rani V., (2013) “A Brief study of various noise models and filtering techniques,” Journal of Global Research in Computer Science, Vol. 4, No. 4.
  34. T. Chhabra, G. Dua and T. Malhotra (2013) “Comparative Analysis of Denoising Methods in CT Images” International Journal of Emerging Trends inElectrical and Electronics, Vol. 3, Issue 2.
  35. Alexei Lufkin,” Tips& Tricks: Fast Image Filtering Algorithms”Moscow State University, Moscow, Russia.
  36. http://www.owlnet.rice.edu/~elec539/Projects99/BACH/proj2/wiener.html
  37. D. Maheswari et. al. NOISE REMOVAL IN COMPOUND IMAGE USING MEDIAN FILTER. (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 0 4, 2010, 1359-1362
  38. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3D transform-domain collaborative filtering,” IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080–2095, Aug. 2007.
  39. Aram Danielyan, Vladimir Katkovnik, and Karen Egiazarian, Senior Member, IEEE “BM3D frames and variational image deblurring” , Image Processing, IEEE Transactions on (Volume:21 , Issue: 4 ), ISSN: 1057-7149
  40. Amit Kumar Singh, Nomit Sharma, Mayank Dave, Anand Mohan, ―A Novel Technique for Digital Image Watermarking in Spatial Domain‖, 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing.
  41. http://en.wikipedia.org/wiki/Signal_to_noise_ratio_(imaging)
  42. Amit Kumar Singh, Nomit Sharma, Mayank Dave, Anand Mohan, ―A Novel Technique for Digital Image Watermarking in Spatial Domain‖, 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing.
  43. http://www.digimizer.com/manual/m-image-filtermax.php
  44. http://www.digimizer.com/manual/m-image-filtermin.php
  45. http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/VELDHUIZEN/node18.html
Index Terms

Computer Science
Information Sciences

Keywords

Noise Models Filters Noise removal techniques Image restoration.