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Revistas
Applied Mathematics and Nonlinear Sciences
Volumen 2 (2017): Edición 1 (January 2017)
Acceso abierto
Smoothing vs. sharpening of colour images: Together or separated
Cristina Pérez-Benito
Cristina Pérez-Benito
,
Samuel Morillas
Samuel Morillas
,
Cristina Jordán
Cristina Jordán
y
J. Alberto Conejero
J. Alberto Conejero
| 30 jun 2017
Applied Mathematics and Nonlinear Sciences
Volumen 2 (2017): Edición 1 (January 2017)
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Publicado en línea:
30 jun 2017
Páginas:
299 - 316
Recibido:
16 feb 2017
Aceptado:
30 jun 2017
DOI:
https://doi.org/10.21042/AMNS.2017.1.00025
Palabras clave
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.