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

by D. Mary sugantharathnam, Dr. D. Manimegalai
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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
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Index Terms

Computer Science
Information Sciences

Keywords

Curvelet transform Wrapping Transform USFFT

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