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Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters

This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection, in which the so-called gray-level difference and average background difference are considered as the inputs of a WN. Wavelet Network is used as a...

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Autores principales: Sadri, Amir Reza, Zekri, Maryam, Sadri, Saeid, Gheissari, Niloofar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3592502/
https://www.ncbi.nlm.nih.gov/pubmed/23493998
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author Sadri, Amir Reza
Zekri, Maryam
Sadri, Saeid
Gheissari, Niloofar
author_facet Sadri, Amir Reza
Zekri, Maryam
Sadri, Saeid
Gheissari, Niloofar
author_sort Sadri, Amir Reza
collection PubMed
description This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection, in which the so-called gray-level difference and average background difference are considered as the inputs of a WN. Wavelet Network is used as a preprocessing for the second stage. The second step is removing impulse noise with a median filter. The wavelet network presented here is a fixed one without learning. Experimental results show that our method acts on impulse noise effectively, and at the same time preserves chromaticity and image details very well.
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spelling pubmed-35925022013-03-14 Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters Sadri, Amir Reza Zekri, Maryam Sadri, Saeid Gheissari, Niloofar J Med Signals Sens Original Article This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection, in which the so-called gray-level difference and average background difference are considered as the inputs of a WN. Wavelet Network is used as a preprocessing for the second stage. The second step is removing impulse noise with a median filter. The wavelet network presented here is a fixed one without learning. Experimental results show that our method acts on impulse noise effectively, and at the same time preserves chromaticity and image details very well. Medknow Publications & Media Pvt Ltd 2012 /pmc/articles/PMC3592502/ /pubmed/23493998 Text en Copyright: © Journal of Medical Signals and Sensors http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Sadri, Amir Reza
Zekri, Maryam
Sadri, Saeid
Gheissari, Niloofar
Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters
title Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters
title_full Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters
title_fullStr Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters
title_full_unstemmed Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters
title_short Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters
title_sort impulse noise cancellation of medical images using wavelet networks and median filters
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3592502/
https://www.ncbi.nlm.nih.gov/pubmed/23493998
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