Connexion
S'inscrire
Réinitialiser le mot de passe
Publier & Distribuer
Solutions d'édition
Solutions de distribution
Thèmes
Architecture et design
Arts
Business et économie
Chimie
Chimie industrielle
Droit
Géosciences
Histoire
Informatique
Ingénierie
Intérêt général
Linguistique et sémiotique
Littérature
Mathématiques
Musique
Médecine
Pharmacie
Philosophie
Physique
Sciences bibliothécaires et de l'information, études du livre
Sciences des matériaux
Sciences du vivant
Sciences sociales
Sport et loisirs
Théologie et religion
Études classiques et du Proche-Orient ancient
Études culturelles
Études juives
Publications
Journaux
Livres
Comptes-rendus
Éditeurs
Blog
Contact
Chercher
EUR
USD
GBP
Français
English
Deutsch
Polski
Español
Français
Italiano
Panier
Home
Journaux
Applied Mathematics and Nonlinear Sciences
Édition 2 (2017): Edition 1 (January 2017)
Accès libre
Smoothing vs. sharpening of colour images: Together or separated
Cristina Pérez-Benito
Cristina Pérez-Benito
,
Samuel Morillas
Samuel Morillas
,
Cristina Jordán
Cristina Jordán
et
J. Alberto Conejero
J. Alberto Conejero
| 30 juin 2017
Applied Mathematics and Nonlinear Sciences
Édition 2 (2017): Edition 1 (January 2017)
À propos de cet article
Article précédent
Article suivant
Résumé
Article
Figures et tableaux
Références
Auteurs
Articles dans cette édition
Aperçu
PDF
Citez
Partagez
Publié en ligne:
30 juin 2017
Pages:
299 - 316
Reçu:
16 févr. 2017
Accepté:
30 juin 2017
DOI:
https://doi.org/10.21042/AMNS.2017.1.00025
Mots clés
Color images
,
image smoothing
,
image sharpening
© 2017 Cristina Pérez-Benito, Samuel Morillas, Cristina Jordán, J. Alberto Conejero, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Fig. 1
Results under different smoothing methods applied to Lenna image corrupted by a Gaussian noise with standard deviation σ = 10.
Fig. 2
Results under different smoothing methods applied to Parrots image corrupted by a Gaussian noise with standard deviation σ = 20.
Fig. 3
Histogram of a gray-scale image of Lenna and the histogram of the resulting image after having applied HE.
Fig. 4
Comparation between a gray-scale image of Lenna and the resulting image after having applied HE.
Fig. 5
Comparation between HE applied over RGB channels separately and over L channel in Lab space.
Fig. 6
Original image of Lenna and the output images obtained after applying BPDFHE and CLAHE methods.
Fig. 7
First row, original Parrot image, filtered with UM and with CLAHE. Second row, a little detail region.
Fig. 8
First row, original image, original image blurred with Gaussian noise with σ = 10, filtered image with BF and finally ouput of BF and posterior CLAHE. Second row, original and noisy image, the enhanced image with CLAHE and finally output of CLAHE and posterior BF.
Fig. 9
First row, original images and original images blurred with Gaussian noise with σ = 10. Second row, the result of applying CLAHE and subsequently BF to both images and then the opposite approach, BF and subsequently CLAHE.
Fig. 10
Denoising results for Lenna image corrupted by Gaussian noise with standard deviation σ = 20.
Fig. 11
Results of smoothing & sharpening with different methods an image corrupted by Gaussian noise with σ = 30.
Fig. 12
First row, Lenna image corrupted by Gaussian noise with standard deviations σ = 10, σ = 20 and σ = 30. Second row, the output of Fuzzy Network filter. Third row, output of BM3DShar and in the last one, the output of TeD-FAB.