Pre-filter that incorporates the noise model

A linear denoising filter is usually of lowpass type, and the main parameter in a lowpass filter is the cutoff frequency. The lowpass filters are normally shift invariant and can be implemented as convolution in the spatial domain or as multiplication in the Fourier domain. This paper presents a lin...

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Detalles Bibliográficos
Autor principal: Zeng, Gengsheng L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2020
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242545/
https://www.ncbi.nlm.nih.gov/pubmed/32440712
http://dx.doi.org/10.1186/s42492-020-00051-z
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author Zeng, Gengsheng L.
author_facet Zeng, Gengsheng L.
author_sort Zeng, Gengsheng L.
collection PubMed
description A linear denoising filter is usually of lowpass type, and the main parameter in a lowpass filter is the cutoff frequency. The lowpass filters are normally shift invariant and can be implemented as convolution in the spatial domain or as multiplication in the Fourier domain. This paper presents a linear filter that is not characterized by its cutoff frequency but is characterized by the noise model. An example of such a linear filter is presented for low-dose X-ray computed tomography (CT).
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spelling pubmed-72425452020-06-03 Pre-filter that incorporates the noise model Zeng, Gengsheng L. Vis Comput Ind Biomed Art Original Article A linear denoising filter is usually of lowpass type, and the main parameter in a lowpass filter is the cutoff frequency. The lowpass filters are normally shift invariant and can be implemented as convolution in the spatial domain or as multiplication in the Fourier domain. This paper presents a linear filter that is not characterized by its cutoff frequency but is characterized by the noise model. An example of such a linear filter is presented for low-dose X-ray computed tomography (CT). Springer Singapore 2020-05-22 /pmc/articles/PMC7242545/ /pubmed/32440712 http://dx.doi.org/10.1186/s42492-020-00051-z Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Article
Zeng, Gengsheng L.
Pre-filter that incorporates the noise model
title Pre-filter that incorporates the noise model
title_full Pre-filter that incorporates the noise model
title_fullStr Pre-filter that incorporates the noise model
title_full_unstemmed Pre-filter that incorporates the noise model
title_short Pre-filter that incorporates the noise model
title_sort pre-filter that incorporates the noise model
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242545/
https://www.ncbi.nlm.nih.gov/pubmed/32440712
http://dx.doi.org/10.1186/s42492-020-00051-z
work_keys_str_mv AT zenggengshengl prefilterthatincorporatesthenoisemodel

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