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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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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. |
format | Online Article Text |
id | pubmed-7297579 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT foreromanuelg improvementoftheturajlicmethodfortheestimationofgaussiannoiseinimages AT mirandasergiol improvementoftheturajlicmethodfortheestimationofgaussiannoiseinimages AT jacanamejoyjamioycarlos improvementoftheturajlicmethodfortheestimationofgaussiannoiseinimages |