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

Weighted Non-Linear Diffusion Filtering with Wavelet Thresholding in Image Denoising

by Reena Singh, V. K. Srivastava
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 78 - Number 14
Year of Publication: 2013
Authors: Reena Singh, V. K. Srivastava
10.5120/13588-1318

Reena Singh, V. K. Srivastava . Weighted Non-Linear Diffusion Filtering with Wavelet Thresholding in Image Denoising. International Journal of Computer Applications. 78, 14 ( September 2013), 1-6. DOI=10.5120/13588-1318

@article{ 10.5120/13588-1318,
author = { Reena Singh, V. K. Srivastava },
title = { Weighted Non-Linear Diffusion Filtering with Wavelet Thresholding in Image Denoising },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 78 },
number = { 14 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume78/number14/13588-1318/ },
doi = { 10.5120/13588-1318 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:51:32.348012+05:30
%A Reena Singh
%A V. K. Srivastava
%T Weighted Non-Linear Diffusion Filtering with Wavelet Thresholding in Image Denoising
%J International Journal of Computer Applications
%@ 0975-8887
%V 78
%N 14
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Wavelet based image denoising is an important technique in the area of image noise reduction. In this paper, a new adaptive wavelet based image denoising algorithm in the presence of Gaussian noise is developed. In the existing wavelet thresholding methods, the final noise reduced image has limited improvement. It is due to keeping the approximate wavelet coefficients unchanged. Since noise affects both approximate as well as detail coefficients, the proposed technique incorporates methods to eliminate noise in both types of coefficients. The propose technique is applied in two phases. In the first phase, an adaptive data driven threshold for image denoising via wavelet soft-thresholding is applied on detail coefficients. In the second phase of the proposed algorithm, anisotropic diffusion is applied on approximate coefficients. In this context, a weighted diffusivity function is proposed which incorporates contextual discontinuities in the image. The diffusivity function derived is applied depending on local image features and hence improve the capability of feature preservation along with noise removal. The proposed technique was applied on standard noisy image and the results obtained show the superiority of the method over other wavelet based denoising techniques.

References
  1. Senel H. G. ,Peters R. A. ,Dawant B. : "Topological median filters", IEEE Trans. Image Process, 2002,11,(2),pp. 89-104.
  2. Chang S. G. , YU B. , Vetterli M. : 'Adaptive wavelet thresholding for image denoising and compression', IEEE Trans. Image Process. , 2000, 9, (9), pp. 1532–1545
  3. Catte, F. , Lions, P. L. , Morel, J. M. , Andcoll, T. : 'Image selective smoothing and edge detection by nonlinear diffusion', SIAM J. Numer. Anal. , 1992, 29, (1), pp. 182–193
  4. Black, M. J. , Shapiro, G. , MARIMONT, D. H. , AND Heeger, D. : 'Robust anisotropic diffusion', IEEE Trans. Image Process. , 1998, 7, (3),pp. 421–432
  5. Yu, J. H. , Wang, Y. Y. , and Shen, Y. Z. : 'Noise reduction and edge detection via kernel anisotropic diffusion', Pattern Recognition. Lett. ,2008, 29, (10), pp. 1496–1503
  6. Canny, J. : 'A computational approach to edge detection', IEEE Trans. Pattern Anal. Mach. Intel. , 1986, 8, (6), pp. 679–697
  7. S. G. Chang, B. Yu, and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression," IEEE Trans. Image Process. , vol. 9, no. 9, pp. 1532–1546, Sep. 2000.
  8. S. G. Chang, Y. Bin, and M. Vetterli, "Spatially adaptive wavelet thresholding with context modeling for image denoising," IEEE Trans. Image Process. , vol. 9, no. 9, pp. 1522–1531, Sept. 2000.
  9. D. L. Donoho and I. M. Johnstone, "Ideal spatial adaption via wavelet shrinkage," Biometrika, vol. 81, pp. 425–455, 1994.
  10. S. Beheshti and M. A. Dahleh, "A new information-theoretic approach to signal denoising and best basis selection," IEEE Trans. Signal Processing, vol. 53, no. 10, pp. 3613–3624, Oct. 2005.
  11. T. Blu and F. Luisier, "The SURE-LET approach to image denoising,"IEEE Trans. Image Process. , vol. 16, no. 11, pp. 2778–2786, Nov. 2007.
  12. Gonzalez R. C. Woods: 'Digital image processing'(Prentice-Hall, 2004)
  13. Aubert g. , kornprobst p. : 'Mathematical problems in image processing: partial differential equations and calculus of variations' (Springer, New York, 2005)
  14. Chang S. G. , Yu b. , Vettereli m. : 'Adaptive wavelet thresholding for image denoising and compression', IEEE Trans. Image Process. , 2000, 9, (9), pp. 1532–1546
  15. You Y. -L. , Xu W. , Tannenbaum A. , Kaveh M. : 'Behavioral analysis of anisotropic diffusion in image processing', IEEE Trans. Image Process. , 1996, 5, (11), pp. 1539–1553
  16. Donoho D. L. : 'Denoising by soft thresholding', IEEE Trans. Inf. Theory, 1995, 41, (3), pp. 933–936
  17. M. Nikpour and H. Hassanpour," Using diffusion equations for improving performance of wavelet based image denoising techniques",IET Image Process. ,2010. Vol. 4,Iss. 6,pp. 452-462
  18. H. C. Li, P. Z. Fan and M. K. Khan," Context-adaptive anisotropic diffusion for image denoising", ELECTRONICS LETTRES 5th July 2012 Vol. 48 N0. 14
  19. Feng Liu and Jingbo Liu,"Anisotropic diffusion for image denoising based on diffusion tensors", J. Vis. Commun. Image R. 23(2012)16-521
  20. J. Wickert," Theoretical foundations of anisotropic diffusion in image processing",Comput 221-236 . Suppl. ,11 (1996)
  21. Wenhua Ma,Yu-Li You and M. Kaveh,"Monotonically edge-sharpening anisotropic diffusion",Journal of Electronic Imaging21(1),013008(Jan-Mar2012)
Index Terms

Computer Science
Information Sciences

Keywords

Wavelet thresholding BayesShrink anisotropic diffusion weighted diffusivity function denoising wavelet coefficients.

Powered by PhDFocusTM