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
Implementation of Feature Selection Using Correlation Matrix in Python

Ahmad Farhan AlShammari
Random Articles
Reseach Article

An Improved Adaptive Wavelet Thresholding Image Denoising Method

Published on February 2015 by R Vijaya Arjunan, B Kishore, N Sivaselvan
journal_cover_thumbnail

Advanced Computing and Communication Techniques for High Performance Applications
Foundation of Computer Science USA
ICACCTHPA2014 - Number 2
February 2015
Authors: R Vijaya Arjunan, B Kishore, N Sivaselvan
d9bfbd91-6853-4548-a7a3-c93b07243f7e

R Vijaya Arjunan, B Kishore, N Sivaselvan . An Improved Adaptive Wavelet Thresholding Image Denoising Method. Advanced Computing and Communication Techniques for High Performance Applications. ICACCTHPA2014, 2 (February 2015), 23-27.

@article{
author = { R Vijaya Arjunan, B Kishore, N Sivaselvan },
title = { An Improved Adaptive Wavelet Thresholding Image Denoising Method },
journal = { Advanced Computing and Communication Techniques for High Performance Applications },
issue_date = { February 2015 },
volume = { ICACCTHPA2014 },
number = { 2 },
month = { February },
year = { 2015 },
issn = 0975-8887,
pages = { 23-27 },
numpages = 5,
url = { /proceedings/icaccthpa2014/number2/19441-6022/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Advanced Computing and Communication Techniques for High Performance Applications
%A R Vijaya Arjunan
%A B Kishore
%A N Sivaselvan
%T An Improved Adaptive Wavelet Thresholding Image Denoising Method
%J Advanced Computing and Communication Techniques for High Performance Applications
%@ 0975-8887
%V ICACCTHPA2014
%N 2
%P 23-27
%D 2015
%I International Journal of Computer Applications
Abstract

The NeighShrink, IAWDMBNC, and IIDMWT are some familiar methods for noise minimization from corrupted image. However, this mentioned method suffers from optimal recovery of the original image since the threshold value does not minimize the noisy wavelet coefficients across the image scale factor. In this paper, we propose an improved denoising method that provides an adaptive way of setting up minimum threshold by shrinking the wavelet coefficients so as to overcome the above problem using a new modified exponential function. The experimental analysis qualifying image such as Peak to Signal Noise ratio (PSNR) and Structural Similarity Index Measure (SSIM) are found better than the NeighShrink, IAWDMBNC, and IIDMWT methods. Moreover, our method retains the original image information with high visual quality.

References
  1. R. C. Gonzalez and R. E. Woods, Digital Image Processing. Reading, MA: Addison-Wesley, 1993.
  2. T. Edwards, "Discrete Wavelet Transforms: Theory and Implementation," Discrete Wavelet Transforms, Stanford University, 1992.
  3. A. Graps, "An Introduction to Wavelets," IEEE Computational Science and Engineering Vol. 2, No. 2, 1995.
  4. D. L. Donoho and I. M. Johnstone, "Ideal spatial adaptation via wavelet shrinkage," Biometrika, Vol. 81, No. 3, 1994, pp. 425- 455.
  5. D. L. Donoho and I. M. Johnstone, "Adapting to Unknown Smoothness via Wavelet Shrinkage," Journal of American Statistical Association, Vol. 90, No. 432, 1995, pp. 1200 1224.
  6. D. L. Donoho, "De-Noising by Soft Thresholding," IEEE Trans. Information Theory, Vol. 41, No. 3, 1995, pp. 613–627.
  7. T. T. Cai and B. W. Silverman, "Incorporating information on neighboring coefficients into wavelet estimation," Sankhya: Ind. J. Stat. B, Pt. 2, Vol. 63, 2001, pp. 127–148.
  8. G. Y. Chen and T. D. Bui. "Multiwavelets Denoising Using Neighboring Coefficients," IEEE Signal Processing Letters, Vol. 10, No. 7, 2003, pp. 211-214.
  9. G. Y. Chen, T. D. Bui and A. Krzyzak, "Image Denoising Using Neighbouring Wavelet Coefficients," ICASSP, 2004, pp. 917-920.
  10. B. C. Rao and M. M. Latha, "Selective Neighbouring Wavelet Coefficients Approach For Image Denoising," International Journal of Computer Science and Communication, Vol. 2, No. 1, 2011, pp. 73– 77.
  11. S. K. Mohideen, S. A. Perumal and M. M. Sathik, "Image De-noising using Discrete Wavelet transform", International Journal of Computer Science and Network Security, Vol. 8, No. 1, 2008, pp. 213-216.
  12. H. Om and M. Biswas, "An Improved Image Denoising Method based on Wavelet Thresholding (IIDMWT)," Journal of Signal and Information Processing (USA), Vol. 3, No. 1, 2012, pp. 109-116.
  13. J. Jiang, J. Guo, W. Fan, and Q. Chen, "An Improved Adaptive Wavelet Denoising Method Based on Neighboring Coefficients" World Congress on Intelligent Control and Automation, China, 2010, pp. 2894-2898.
  14. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity", IEEE Trans. on Image Processing, Vol. 13, No. 4, 2004, pp. 600-612.
  15. Krishna veni . G, Nagarjuna Reddy. R, Dhanaraj Cheelu3, "An Adaptive Wavelet Thresholding Image Denoising Method Using Neighboring Wavelet Coefficients", International Journal of Innovative Research in Engineering & Science, ISSN 2319-5665, issue 2 volume 9, September 2013, pp: 46-60.
Index Terms

Computer Science
Information Sciences

Keywords

Image Noise Wavelet Transform Thresholding Psnr Ssim