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Diffusion-Driven X-Ray Two-Dimensional Patterns Denoising

The use of a mathematical model is proposed in order to denoise X-ray two-dimensional patterns. The method relies on a generalized diffusion equation whose diffusion constant depends on the image gradients. The numerical solution of the diffusion equation provides an efficient reduction of pattern n...

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Detalles Bibliográficos
Autores principales: Ladisa, Massimo, Lamura, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7344928/
https://www.ncbi.nlm.nih.gov/pubmed/32570931
http://dx.doi.org/10.3390/ma13122773
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author Ladisa, Massimo
Lamura, Antonio
author_facet Ladisa, Massimo
Lamura, Antonio
author_sort Ladisa, Massimo
collection PubMed
description The use of a mathematical model is proposed in order to denoise X-ray two-dimensional patterns. The method relies on a generalized diffusion equation whose diffusion constant depends on the image gradients. The numerical solution of the diffusion equation provides an efficient reduction of pattern noise as witnessed by the computed peak of signal-to-noise ratio. The use of experimental data with different inherent levels of noise allows us to show the success of the method even in the case, experimentally relevant, when patterns are blurred by Poissonian noise. The corresponding MatLab code for the numerical method is made available.
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spelling pubmed-73449282020-07-09 Diffusion-Driven X-Ray Two-Dimensional Patterns Denoising Ladisa, Massimo Lamura, Antonio Materials (Basel) Article The use of a mathematical model is proposed in order to denoise X-ray two-dimensional patterns. The method relies on a generalized diffusion equation whose diffusion constant depends on the image gradients. The numerical solution of the diffusion equation provides an efficient reduction of pattern noise as witnessed by the computed peak of signal-to-noise ratio. The use of experimental data with different inherent levels of noise allows us to show the success of the method even in the case, experimentally relevant, when patterns are blurred by Poissonian noise. The corresponding MatLab code for the numerical method is made available. MDPI 2020-06-18 /pmc/articles/PMC7344928/ /pubmed/32570931 http://dx.doi.org/10.3390/ma13122773 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ladisa, Massimo
Lamura, Antonio
Diffusion-Driven X-Ray Two-Dimensional Patterns Denoising
title Diffusion-Driven X-Ray Two-Dimensional Patterns Denoising
title_full Diffusion-Driven X-Ray Two-Dimensional Patterns Denoising
title_fullStr Diffusion-Driven X-Ray Two-Dimensional Patterns Denoising
title_full_unstemmed Diffusion-Driven X-Ray Two-Dimensional Patterns Denoising
title_short Diffusion-Driven X-Ray Two-Dimensional Patterns Denoising
title_sort diffusion-driven x-ray two-dimensional patterns denoising
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7344928/
https://www.ncbi.nlm.nih.gov/pubmed/32570931
http://dx.doi.org/10.3390/ma13122773
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AT lamuraantonio diffusiondrivenxraytwodimensionalpatternsdenoising