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

A Discontinuity Adaptive Prior for Image Denoising

by Aravind B N, K V Suresh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 110 - Number 2
Year of Publication: 2015
Authors: Aravind B N, K V Suresh
10.5120/19288-0710

Aravind B N, K V Suresh . A Discontinuity Adaptive Prior for Image Denoising. International Journal of Computer Applications. 110, 2 ( January 2015), 13-19. DOI=10.5120/19288-0710

@article{ 10.5120/19288-0710,
author = { Aravind B N, K V Suresh },
title = { A Discontinuity Adaptive Prior for Image Denoising },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 110 },
number = { 2 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 13-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume110/number2/19288-0710/ },
doi = { 10.5120/19288-0710 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:45:19.520594+05:30
%A Aravind B N
%A K V Suresh
%T A Discontinuity Adaptive Prior for Image Denoising
%J International Journal of Computer Applications
%@ 0975-8887
%V 110
%N 2
%P 13-19
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The presence of noise in digital images degrades the visual quality by corrupting the information associated with the image. The aim of denoising is to restore an image from its noisy version by preserving signal information. In this paper, we are considering an image corrupted by additive Gaussian noise. The image is modeled as Markov random field (MRF) and an estimation of maximum-a-posteriori (MAP) is obtained using graduated non-convexity. The results are compared with other spatial domain filtering methods. The discontinuity adaptive prior helps in preserving edge information. The results suggest that proposed method has an improved performance.

References
  1. D. L. Donoho. 1995. Denoising by soft threhsolding. IEEE Trans. on Information Theory. (May 1995), 613-627.
  2. R. R. Coifman and D. L. Donoho. 1995. Translation invariant denoising. Wavelet and Statistics, Springer Lecture Notes in Statistics, Springer-Verlag. (1995), 125-150.
  3. T. Tony cai and Bernard W. Silverman. 2001. Incorporating information on neighbouring wavelet coefficients. Sankhya. (Ser. B63 2001), 127-148, 2001.
  4. Zhou dengwen and Cheng Wengang. 2008. Image denoising with an optimal threhsolding and neighbouring window. Pattern Recognition Letters, Elsevier. (Aug. 2008), 1694-1697.
  5. D. Cho, T. D. Bui and G. Chen. 2009. Image denoising based on wavelet shrinkage using neighbour and level dependency. International Journal of Wavelets, Multiresolution and Information Processing. (2009), 299-311.
  6. Antoni Buades, Bartoneu Coll and Jean Michel Morel. 2008. Non-local image and movie denoising. International Journal of Computer Vision, Springer. (Feb 2008), 123-139.
  7. R. Wallis. 1976. An approach to the space variant restoration and enhancement of images. Symposium on Current Mathematical Problems in Image Science, Naval postgraduate school, Monterey, CA.
  8. Jong-Sen Lee. 1980. Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. on Pattern Analysis and Machine Vision. (March 1980), 165-168.
  9. F. Jin, P. Fifguth, L. Winger and E. Jernigan. 2003 Adaptive Wiener filtering of noisy images and image sequences. In Proceedings of IEEE International Conference on Image Processing. III-349-352.
  10. D. T. Kaun, A. A. Sawchuk, T. C. Strand and P. Chavel. 1985. Adaptive noise smoothing filter for images with signal dependent noise. IEEE Trans. on Pattern Analysis and Machine Vision, PAMI-7. (March 1985), 165-177.
  11. C. Tomasi and R. Manduchi. 1998. Bilateral filtering for gray and color images. In Proceedings of IEEE International Conference on Computer Vision, Bombay, India.
  12. Rafel C. Gonzalez and Richars E. Woods. 2009. Digital image processing. Pearson education.
  13. D. Barash 2002. A fundamental relationship between bilateral filtering, adaptive smoothing and the nonlinear diffusion equation. IEEE Trans. on Pattern Analysis and Machine Vision. (June 2002), 844-847.
  14. Wilbur C. K. Wong, Albert C. S. Chung and Simon C. H. Yu. 2004. Trilateral filtering for biomedical images. In Proceedings of IEEE International Symposium on Biomedical Imaging: Nano to Micro.
  15. L. Rudin and Stanley Osher. 1994. Total variation based image restoration with free local constraints. In Proceedings of IEEE International Conference on Image Processing, Austin Tx.
  16. Tony F. Chen, Stanley Osher and Jianhong Shen. 2001. The digital TV filter and nonlinear denoising. IEEE Trans. on Image Processing. (Feb. 2001), 231-241.
  17. Antoni Buades, Bartoneu Coll and Jean Michel Morel. 2004. On image denoising methods. SIAM Review.
  18. Antoni Buades, Bartoneu Coll and Jean Michel Morel. 2005. Image denoising by non-local averaging. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing.
  19. Antoni Buades, Bartoneu Coll and Jean Michel Morel. 2010. Image denoising methods, a new non-local principle. SIAM Review.
  20. Mona Mahmoudi and Guillerno Sapiro. 2005. Fast image and video denoising via non-local means of similar neighbourhoods. IEEE Signal Processing Letters. (Dec. 2005), 839-842.
  21. R. L. Kashyap and R. Chellappa. 1981. Filtering of noisy images using Markov random field models. In Proceedings of 19th Allerton Conference on Communication Control and Computing, Urbana, Illinois.
  22. S. Geman and D. Geman. 1984. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence. (Nov. 1984), 721-741.
  23. J. Besag. 1986. On the statistical analysis of dirty pictures. Journal of Royal Statistical Society, (1986), 259-302.
  24. J. Marroquin, S. Mitter, and T. Poggio. 1987. Probabilistic solutions of ill-posed problems in computational vision. Journal of American Statistical Association. (March 1987), 76-89.
  25. R. Szeliski. 1990. Bayesian modeling of uncertainty in low-level vision. International Journal on Computer Vision. (1990), 271-301.
  26. G. Sebastiani and F. Godtliebsen. 1997. On the use of Gibbs priors for Bayesian image restoration. Signal Processing. (Jan. 1997), 111-118.
  27. J. Sun, N. -N. Zhen, and H. -Y. Shum. 2003. Stereo matching using belief propagation. IEEE Trans. Pattern Analysis and Machine Intelligence. (2003), 787-800.
  28. F. Heitz and P. Bouthemy. 1993. Multimodal estimation of discontinuous optical flow using Markov random fields. IEEE Trans. on Pattern Analysis and Machine Intelligence. (Dec. 1993), 1217-1232.
  29. M. Varma and A. Zisserman. 2005. A statistical approach to texture classification from single images. International Journal of Computer Vision, (April 2005), 61-81.
  30. J. Besag. 1974. Spatial interaction and the statistical analysis of lattice system. Journal of the Royal Statistical Society, Series B. (1974), 192-236.
  31. K. V. Suresh, G. Mahesh kumar and A. N. Rajagopalan. 2007. Super-resolution of license plates in real traffic videos. IEEE Trans. on Intelligent Transportation Systems. (June 2007), 321-331.
  32. S. Z. Li. 1995. Markov random field modeling in computer vision. Springer-Verlag.
  33. A. Blake and A. Zisserman. 1987. Visual reconstruction. MIT press.
  34. K. N. Chaudhury, D. Sage, and M. Unser. 2011. Fast O(1) bilateral filtering using trigonometric range kernels. IEEE Trans. on Image Processing. (June 2011), 3376-3382.
  35. Zhou wang, Alan Conrad bovik, Hamid Rahim sheikh and Eero P. Simoncelli. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Trans. on Image Processing. (April 2004), 600-612.
Index Terms

Computer Science
Information Sciences

Keywords

Image denoising Markov random field Discontinuity adaptive Graduated non-convexity.

Powered by PhDFocusTM