Graphs based methods for simultaneous smoothing and sharpening
We present two new methods for simultaneous smoothing and sharpening of color images: the GMS3 (Graph Method for Simultaneous Smoothing and Sharpening) and the NGMS3(Normalized Graph-Method for Simultaneous Smoothing and Sharpening). They are based on analyzing the structure of local graphs computed...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-01-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S221501612030039X |
| _version_ | 1818325433309986816 |
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| author | Cristina Pérez-Benito Cristina Jordán J. Alberto Conejero Samuel Morillas |
| author_facet | Cristina Pérez-Benito Cristina Jordán J. Alberto Conejero Samuel Morillas |
| author_sort | Cristina Pérez-Benito |
| collection | DOAJ |
| description | We present two new methods for simultaneous smoothing and sharpening of color images: the GMS3 (Graph Method for Simultaneous Smoothing and Sharpening) and the NGMS3(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. |
| first_indexed | 2024-12-13T11:44:24Z |
| format | Article |
| id | doaj.art-738c7bceb53b48c2a813c623234a142f |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-12-13T11:44:24Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-738c7bceb53b48c2a813c623234a142f2022-12-21T23:47:33ZengElsevierMethodsX2215-01612020-01-017100819Graphs based methods for simultaneous smoothing and sharpeningCristina Pérez-Benito0Cristina Jordán1J. Alberto Conejero2Samuel Morillas3Instituto de Biomecánica de València, Universitat Politècnica de València, E-46022 València, SpainInstituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, E-46022 València, SpainInstituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, E-46022 València, Spain; Corresponding author.Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, E-46022 València, SpainWe present two new methods for simultaneous smoothing and sharpening of color images: the GMS3 (Graph Method for Simultaneous Smoothing and Sharpening) and the NGMS3(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.http://www.sciencedirect.com/science/article/pii/S221501612030039XColor image processingLocal graphsSimultaneous smoothing and sharpening |
| spellingShingle | Cristina Pérez-Benito Cristina Jordán J. Alberto Conejero Samuel Morillas Graphs based methods for simultaneous smoothing and sharpening MethodsX Color image processing Local graphs 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 | Color image processing Local graphs Simultaneous smoothing and sharpening |
| url | http://www.sciencedirect.com/science/article/pii/S221501612030039X |
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