CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Analysing Image Denoising using Non Local Means Algorithm

by Deepak Raghuvanshi, Shabahat Hasan, Mridula Agrawal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 13
Year of Publication: 2012
Authors: Deepak Raghuvanshi, Shabahat Hasan, Mridula Agrawal
10.5120/8949-3130

Deepak Raghuvanshi, Shabahat Hasan, Mridula Agrawal . Analysing Image Denoising using Non Local Means Algorithm. International Journal of Computer Applications. 56, 13 ( October 2012), 7-11. DOI=10.5120/8949-3130

@article{ 10.5120/8949-3130,
author = { Deepak Raghuvanshi, Shabahat Hasan, Mridula Agrawal },
title = { Analysing Image Denoising using Non Local Means Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 13 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number13/8949-3130/ },
doi = { 10.5120/8949-3130 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:58:42.942207+05:30
%A Deepak Raghuvanshi
%A Shabahat Hasan
%A Mridula Agrawal
%T Analysing Image Denoising using Non Local Means Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 13
%P 7-11
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Digital image processing remains a challenging domain of programming. All digital images contain some degree of noise. Often times this noise is introduced by the camera when a picture is taken. Image denoising algorithms attempt to remove this noise from the image. In this paper the method for image denoising based on the nonlocal means (NL-means) algorithm has been implemented and results have been developed using matlab coding. The algorithm, called nonlocal means (NLM), uses concept of Self-Similarity. Also images taken from the digital media like digital camera and the image taken from the internet have been compared. The image that is taken from the internet has got aligned pixel than the image taken from digital media. Experimental results are given to demonstrate the superior denoising performance of the NL-means denoising technique over various image denoising benchmarks.

References
  1. Ke Lu , Ning He, Liang Li, "Non-Local Based denoising for medical images,"Computational and Mathematical methods in Medical ,vol. 2012,pp. 7,2012.
  2. H. Takeda, S. Farsiu, and P. Milanfar, "Kernel regression for image processing and reconstruction," IEEE Transactions on image processing 16(2), pp. 349–366, 2007.
  3. B. Goossens, A. Pi?zurica, and W. Philips, "Removal of Correlated Noise by Modeling Spatial Correlations and Interscale Dependencies in the Complex Wavelet Domain," in Proc. of IEEE International Conference on Image Processing (ICIP), pp. 317–320, (San Antonio, Texas, USA), Sept. 2007.
  4. A. Pi?zurica and W. Philips, "Estimating the probability of the presence of a signal of interest in multiresolution single and multiband image denoising," IEEE Transactions on image processing 15(3), pp. 654–665, 2006
  5. A. Buades, B. Coll, and J. Morel. On image denoising methods. Technical Report 2004-15, CMLA, 2004.
  6. A. Buades, B. Coll, and J. Morel. Neighborhood filters and pde's. Technical Report 2005-04, CMLA, 2005.
  7. A. Buades, B. Coll, and J Morel, "A non-local algorithm for image denoising ," IEEE International Conference on Computer Vision and Pattern Recognition, 2005.
  8. A. Buades. NL-means Pseudo-Code http://dmi. uib. es/~tomeucoll/toni/NL-means_code. html
  9. A. Efros and T. Leung. "Texture synthesis by nonparametric sampling. "In Proc . Int. Conf . computer Vision, volume 2, pages 1033-1038, 1999.
  10. Awate SP, Tasdizen T, Whitaker RT. Unsupervised Texture Segmentation with Nonparametric Neighborhood Statistics. ECCV. 2006:494–507.
  11. Huang J, Mumford D. Statistics of natural images and models. ICCV. 1999:541–547.
  12. Lee A, Pedersen K, Mumford D. The nonlinear statistics of high- contrast patches in natural images. IJCV. 2003; 54:83–103.
  13. Mahmoudi M, Sapiro G. Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE Signal Processing Letters. 2005;12(12):839–842.
  14. Portilla J, Strela V, Wainwright M, Simoncelli E. Image denoising using scale mixtures of gaussians in the wavelet domain. IEEE Trans On Image Processing. 2003;12:1338–1351.
  15. L. Rudin and S. Osher, "Total variation based image restoration with free local constraints," in Proc. Of IEEE International Conference on Image Processing (ICIP), 1, pp. 31–35, Nov. 1994.
  16. C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images," in Proceedings International Conference on computer vision, pp. 839–846, 1998.
  17. Mathworks. The Matlab image processing toolbox. http://www. mathworks. com/access/helpdesk/help/toolbox/images/
  18. B. Gustavsson. Matlab Central - gen_susan. http://www. mathworks. com/matlabcentral/files/6842/gen_susan. m.
  19. L. S¸endur and I. Selesnick, "Bivariate shrinkage with local variance estimation," IEEE Signal Processing Letters 9, pp. 438–441, 2002.
  20. J. Portilla, V. Strela, M. Wainwright, and E. Simoncelli, "Image denoising using scale mixtures of Gaussians in the wavelet domain ," IEEE Transactions on image processing 12(11), pp. 1338–1351, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

ASIC Image denoising Non-Local Means (NL-means) Algorithm VHDL

Powered by PhDFocusTM