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Smoothing vs. sharpening of colour images: Together or separated

Cristina Pérez-Benito

Cristina Pérez-Benito

Instituto de Biomecánica de Valencia, Universitat Politècnica de ValènciaValència, Spain
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Pérez-Benito, Cristina
, 
Samuel Morillas

Samuel Morillas

Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de ValènciaValència, Spain
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Morillas, Samuel
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Cristina Jordán

Cristina Jordán

Instituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de ValènciaValència, Spain
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Jordán, Cristina
 and 
J. Alberto Conejero

J. Alberto Conejero

Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de ValènciaValència, Spain
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Conejero, J. Alberto
  
Jun 30, 2017
Smoothing vs. sharpening of colour images: Together or separated's Cover Image
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Published Online: Jun 30, 2017
Page range: 299 - 316
Received: Feb 16, 2017
Accepted: Jun 30, 2017
DOI: https://doi.org/10.21042/AMNS.2017.1.00025
Keywords
© 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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