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Graphs based methods for simultaneous smoothing and sharpening
We present two new methods for simultaneous smoothing and sharpening of color images: the GMS(3) (Graph Method for Simultaneous Smoothing and Sharpening) and the NGMS(3)(Normalized Graph-Method for Simultaneous Smoothing and Sharpening). They are based on analyzing the structure of local graphs comp...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7078387/ https://www.ncbi.nlm.nih.gov/pubmed/32195137 http://dx.doi.org/10.1016/j.mex.2020.100819 |
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author | Pérez-Benito, Cristina Jordán, Cristina Conejero, J. Alberto Morillas, Samuel |
author_facet | Pérez-Benito, Cristina Jordán, Cristina Conejero, J. Alberto Morillas, Samuel |
author_sort | Pérez-Benito, Cristina |
collection | PubMed |
description | We present two new methods for simultaneous smoothing and sharpening of color images: the GMS(3) (Graph Method for Simultaneous Smoothing and Sharpening) and the NGMS(3)(Normalized Graph-Method for Simultaneous Smoothing and Sharpening). They are based on analyzing the structure of local graphs computed at every pixel using their respective neighbors. On the one hand, we define a kernel-based filter for smoothing each pixel with the pixels associated to nodes in its same connected component. On the other hand, we modify each pixel by increasing their differences with respect to the pixels in the other connected components of those local graphs. Our approach is shown to be competitive with respect to other state-of-the-art methods that simultaneously manage both processes. • We provide two methods that carry out the process of smoothing and sharpening simultaneously. • The methods are based on the analysis of the structure of a local graph defined from the differences in the RGB space among the pixels in a 3 × 3 window. • The parameters of the method are adjusted using both observers opinion and the well-known reference image quality assessment BRISQUE (Blind/Referenceless images spatial quality Evaluator) score. |
format | Online Article Text |
id | pubmed-7078387 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-70783872020-03-19 Graphs based methods for simultaneous smoothing and sharpening Pérez-Benito, Cristina Jordán, Cristina Conejero, J. Alberto Morillas, Samuel MethodsX Computer Science We present two new methods for simultaneous smoothing and sharpening of color images: the GMS(3) (Graph Method for Simultaneous Smoothing and Sharpening) and the NGMS(3)(Normalized Graph-Method for Simultaneous Smoothing and Sharpening). They are based on analyzing the structure of local graphs computed at every pixel using their respective neighbors. On the one hand, we define a kernel-based filter for smoothing each pixel with the pixels associated to nodes in its same connected component. On the other hand, we modify each pixel by increasing their differences with respect to the pixels in the other connected components of those local graphs. Our approach is shown to be competitive with respect to other state-of-the-art methods that simultaneously manage both processes. • We provide two methods that carry out the process of smoothing and sharpening simultaneously. • The methods are based on the analysis of the structure of a local graph defined from the differences in the RGB space among the pixels in a 3 × 3 window. • The parameters of the method are adjusted using both observers opinion and the well-known reference image quality assessment BRISQUE (Blind/Referenceless images spatial quality Evaluator) score. Elsevier 2020-02-20 /pmc/articles/PMC7078387/ /pubmed/32195137 http://dx.doi.org/10.1016/j.mex.2020.100819 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Computer Science Pérez-Benito, Cristina Jordán, Cristina Conejero, J. Alberto Morillas, Samuel Graphs based methods for simultaneous smoothing and sharpening |
title | Graphs based methods for simultaneous smoothing and sharpening |
title_full | Graphs based methods for simultaneous smoothing and sharpening |
title_fullStr | Graphs based methods for simultaneous smoothing and sharpening |
title_full_unstemmed | Graphs based methods for simultaneous smoothing and sharpening |
title_short | Graphs based methods for simultaneous smoothing and sharpening |
title_sort | graphs based methods for simultaneous smoothing and sharpening |
topic | Computer Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7078387/ https://www.ncbi.nlm.nih.gov/pubmed/32195137 http://dx.doi.org/10.1016/j.mex.2020.100819 |
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