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

The Curvelet Approach for Denoising in various Imaging Modalities using Different Shrinkage Rules

by D. Mary sugantharathnam, Dr. D. Manimegalai
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 29 - Number 7
Year of Publication: 2011
Authors: D. Mary sugantharathnam, Dr. D. Manimegalai
10.5120/3575-4933

D. Mary sugantharathnam, Dr. D. Manimegalai . The Curvelet Approach for Denoising in various Imaging Modalities using Different Shrinkage Rules. International Journal of Computer Applications. 29, 7 ( September 2011), 36-42. DOI=10.5120/3575-4933

@article{ 10.5120/3575-4933,
author = { D. Mary sugantharathnam, Dr. D. Manimegalai },
title = { The Curvelet Approach for Denoising in various Imaging Modalities using Different Shrinkage Rules },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 29 },
number = { 7 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 36-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume29/number7/3575-4933/ },
doi = { 10.5120/3575-4933 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:15:11.659869+05:30
%A D. Mary sugantharathnam
%A Dr. D. Manimegalai
%T The Curvelet Approach for Denoising in various Imaging Modalities using Different Shrinkage Rules
%J International Journal of Computer Applications
%@ 0975-8887
%V 29
%N 7
%P 36-42
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The images usually bring different kinds of noise in the process of receiving, coding and transmission. In this paper the Curvelet transform is used for de-noising of image. Two digital implementations of the Curvelet transform (a multiscale transform) viz the Unequally Spaced Fast Fourier Transform (USFFT) and the Wrapping Algorithm are used to de-noise images degraded by different types of noises such as Random, Gaussian, Salt and Pepper, Speckle and Poisson noise. This paper aims at the effect the Curvelet transform has in Curvelet shrinkage assuming different types of noise models. A signal to noise ratio as a measure of the quality of de-noising was preferred. The experimental results show that the conventional Curvelet shrinkage approach fails to remove Poisson noise in medical images.

References
  1. D.L.Donoho. 1995. “De-noising by soft-thresholding”, IEEE Transactions on Information Theory, Vol 41, No3, May 1995.
  2. Jean-Lue Starck, Emmanuel J. Candes and David L.Donoho. 2002. “ The Curvelet transform for Image Denoising” IEEE Transactions on Image Processing, Vol 11, No 6, June 2002.
  3. Yeqiu Li, Jianming Lu, Ling Wang. 2005. ”Removing Poisson Noise From Images In Wavelet Domain”,2005 IEEE
  4. E.Candes & D.L.Donoho,”Fast Discreet Curvelet transform”, Stanford University, July2005.
  5. Latha Partheban & R.Subramanian. 2006. ”Medical Image Denoising using X-lets”,2006 IEEE
  6. Prit Naik and shalini Bhatia. 2007. ” Image De-noising using Curvelet transform”, Proceedings of SPIT-IEEE colloquium and International conference, Mumbai, India, Vol.2007
  7. R.Sivakumar. 2007. ” De-noising of Computer Tomography Images using Curvelet transform”, ARPN Journal of Engineering and applied Sciences”, Vol2, No1, February 2007.
  8. Jiang Tao, Zhao Xin. 2008. “Research and Application of Image Denoising Method Based on Curvelet Transform” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences”, Vol XXXVII Part B2, Beijing 2008.
  9. Isabel Rodrigues, Joao Sanches and Jose Bioucas-Dias. 2008. ”Denoising of Medical Images corrupted by Poisson Noise” ICIP 2008: 1756-1759 ...
  10. Jianwei Ma and Gerlind Plonka. 2009. Computing with curvelets, From Image Processing to Turbulent Flows. 1521-9615/09/2009 IEEE, Computing in Science and Engineering,2009
  11. Arnaud De Decker, John Aldo Lee and Michel Verlysen. 2009.”Variants Stabilizing Transformation In Patch-Based Bilateral Filters for Poisson Noise Image Denoising” 2009 IEEE
  12. Al-dahoudAli, Preeti D. Swami and J. Singhai. 2010. ”Modified Curvelet thresholding for Image De-Noising”, Journal of computer Science6(1): 18-23,2010.
  13. Jun Xu, Lei Yang, Dapeng Wu. 2010. ” Ripplet : A new transform for image processing”, J.Vis. commun.Image R. 21(2010) 627-639
  14. Ke Ding. 2010. ”Wavelets, curvelets and Wave Atoms for Image Denoising”,2010 3rd International Congress on Image & Signal Processing(CISP 2010).
  15. RafaelC. Gonzalez and Richard E. Woods,” Digital Image Processing”Second Edition.
Index Terms

Computer Science
Information Sciences

Keywords

Curvelet transform Wrapping Transform USFFT

Powered by PhDFocusTM