CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Image Denoising by Two-Pass of Total Variation Filter

by Dao Nam Anh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 17
Year of Publication: 2014
Authors: Dao Nam Anh
10.5120/17276-7706

Dao Nam Anh . Image Denoising by Two-Pass of Total Variation Filter. International Journal of Computer Applications. 98, 17 ( July 2014), 24-29. DOI=10.5120/17276-7706

@article{ 10.5120/17276-7706,
author = { Dao Nam Anh },
title = { Image Denoising by Two-Pass of Total Variation Filter },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 17 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume98/number17/17276-7706/ },
doi = { 10.5120/17276-7706 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:26:26.773461+05:30
%A Dao Nam Anh
%T Image Denoising by Two-Pass of Total Variation Filter
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 17
%P 24-29
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Total variation based methods are widely applied for image enhancement and particularly for de-noising. The majority of these is designed for a specific noise model. The alternative total variation based approach proposed here can deal with multiple noise models via two-pass iterative algorithm basing on total variation. The first pass is designed for draft denoising and to detect noise region. The second pass restores the noise region by total variation based inpainting. Experiments on Salt & Pepper, Gaussian, Speckle, Poisson, and Impulse noise models demonstrate the effectiveness of the proposed method.

References
  1. L. Rudin, S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms", Phys. D, 60 (1992), pp. 259–268.
  2. T. Le, R. Chartrand, T. Asaki. "A Variational Approach to Constructing Images Corrupted by Poisson Noise," Journal of Mathematical Imaging and Vision, vol. 27(3), pp. 257–263, 2007.
  3. Aubert, G. , Aujol, J. F. , "A variational approach to remove multiplicative noise". J. on Applied Mathematics 68(4), 925–946 (2008).
  4. Jose M. Bioucas-Dias, Mario A. T. Figueiredo, "Total Variation Restoration of Speckled Images Using a Split-Bregman Algorithm", IEEE International Conference on Image Processing, 2009.
  5. S. Masnou and J. Morel. "Level-lines based Disocclusion". In Proc. 5th IEEE Int. Conf. on Image Process. , pages 259{263, Chicago, IL, 1998.
  6. M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester. "Image Inpainting". In Proc. 27th Ann. Conf. on Comput. Graph. & Interactive Tech. , pages 417-427, 2000.
  7. T. Chan and J. Shen. "Mathematical Models of Local Non-texture Inpaintings". SIAM J. Appl. Math. , 62:1019{1043, 2001.
  8. F. Guichard, F. Malgouyres, "Total Variation Based Interpolation", Proceedings of Eusipco 1998.
  9. S. Derin Babacan, Rafael Molina, Aggelos K. Katsaggelos, "Total variation super resolution using a variational approach", Image Processing, 2008. ICIP 2008. 15th IEEE International Conference.
  10. Stanley Osher, Andres Sole, And Luminita Vese, "Image Decomposition And Restoration Using Total Variation Minimization And The H?1 Form", Multiscale Model. Simul. 2003 Society for Industrial and Applied Mathematics, Vol. 1, No. 3, pp. 349–370.
  11. S. Alliney, "A property of the minimum vectors of a regularizing functional defined by means of the absolute norm," IEEE Transactions on Signal Processing, vol. 45, no. 4, pp. 913–917, 1997.
  12. T. F. Chan, S. Esedoglu. "Aspects of total variation regularized L1 function approximation. " SIAM Journal on Applied Mathematics, vol. 65, no. 5, pp. 1817–1837, 2005.
  13. T. Goldstein, S. Osher, "The Split Bregman Method for L1-Regularized Problems", SIAM Journal on Imaging Sciences, vol. 2, no. 2, pp. 323-343, 2009.
  14. Pascal Getreuer, "Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman", Image Processing On Line, 2012. ISSN 2105-1232, 2012 IPOL.
  15. Paul Rodríguez, Renán Rojas and Brendt Wohlberg, "Mixed Gaussian-Impulse Noise Image Restoration via Total Variation", ICASSP, (Kyoto, Japan), pp. 1077-1080, Mar 2012.
  16. R. Chan, C. -W. Ho, and M. Nikolova. "Salt-and pepper noise removal by median-type noise detectors and detail-preserving regularization". IEEE Transactions on Image Processing, vol. 14, no. 10, pp. 1479–1485, 2005.
  17. S. Leung and S. Osher, "Global Minimization of the Active Contour Model with TV-Inpainting and Two-Phase Denoising", Variational, Geometric, and Level Set Methods in Computer Vision (VLSM), Springer, Vol. 3752, pp. 149–160, 2005.
  18. R. Chan, C. Hu, and M. Nikolova. "An iterative procedure for removing random-valued impulse noise," IEEE Signal Process. Letters, pp. 921–924, 2004.
  19. M. Nikolova, "A variational approach to remove outliers and impulse noise," Journal of Mathematical Imaging and Vision, 20 (2004), pp. 99–120.
  20. M. Lindenbaum, M. Fischer, and A. M. Bruckstein, "On gabor contribution to image enhancement", Pattern Recognition, 27 (1994).
  21. T. F. Chan, J. Shen, "Mathematical models of local non-texture inpaintings", SIAM Journal on Applied Mathematics, vol. 62, no. 3, pp. 1019-1043, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

denoising inpainting restoration keeping edge filtering total variation

Powered by PhDFocusTM