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Improvement of the Turajlić Method for the Estimation of Gaussian Noise in Images

Gaussian noise estimation is an important step in some of the more recently developed noise removal methods. This is a difficult task and although several estimation techniques have been proposed recently, they generally do not produce good results. In a previous comparative study, among several noi...

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Autores principales: Forero, Manuel G., Miranda, Sergio L., Jacanamejoy-Jamioy, Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297579/
http://dx.doi.org/10.1007/978-3-030-49076-8_11
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author Forero, Manuel G.
Miranda, Sergio L.
Jacanamejoy-Jamioy, Carlos
author_facet Forero, Manuel G.
Miranda, Sergio L.
Jacanamejoy-Jamioy, Carlos
author_sort Forero, Manuel G.
collection PubMed
description Gaussian noise estimation is an important step in some of the more recently developed noise removal methods. This is a difficult task and although several estimation techniques have been proposed recently, they generally do not produce good results. In a previous comparative study, among several noise estimation techniques, a method proposed in 2017 by Turajlić was found to give the best results. Although acceptable, they are still far from ideal. Therefore, several changes to this method are introduced in this paper to improve the estimation. Tests on monochromatic images contaminated with different levels of Gaussian noise showed that the modified method produces a significant improvement in the estimation of Gaussian noise, over 35%, at a slightly higher computational cost.
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spelling pubmed-72975792020-06-17 Improvement of the Turajlić Method for the Estimation of Gaussian Noise in Images Forero, Manuel G. Miranda, Sergio L. Jacanamejoy-Jamioy, Carlos Pattern Recognition Article Gaussian noise estimation is an important step in some of the more recently developed noise removal methods. This is a difficult task and although several estimation techniques have been proposed recently, they generally do not produce good results. In a previous comparative study, among several noise estimation techniques, a method proposed in 2017 by Turajlić was found to give the best results. Although acceptable, they are still far from ideal. Therefore, several changes to this method are introduced in this paper to improve the estimation. Tests on monochromatic images contaminated with different levels of Gaussian noise showed that the modified method produces a significant improvement in the estimation of Gaussian noise, over 35%, at a slightly higher computational cost. 2020-04-29 /pmc/articles/PMC7297579/ http://dx.doi.org/10.1007/978-3-030-49076-8_11 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Forero, Manuel G.
Miranda, Sergio L.
Jacanamejoy-Jamioy, Carlos
Improvement of the Turajlić Method for the Estimation of Gaussian Noise in Images
title Improvement of the Turajlić Method for the Estimation of Gaussian Noise in Images
title_full Improvement of the Turajlić Method for the Estimation of Gaussian Noise in Images
title_fullStr Improvement of the Turajlić Method for the Estimation of Gaussian Noise in Images
title_full_unstemmed Improvement of the Turajlić Method for the Estimation of Gaussian Noise in Images
title_short Improvement of the Turajlić Method for the Estimation of Gaussian Noise in Images
title_sort improvement of the turajlić method for the estimation of gaussian noise in images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297579/
http://dx.doi.org/10.1007/978-3-030-49076-8_11
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