A nonlinear fractional reaction-diffusion system applied to image denoising and decomposition

Abdelghafour Atlas; Mostafa Bendahmane; Fahd Karami; Driss Meskine; Omar Oubbih

doi:10.3934/dcdsb.2020321

Article Contents

2021, Volume 26, Issue 9: 4963-4998. Doi: 10.3934/dcdsb.2020321

This issue Previous Article On existence and uniqueness properties for solutions of stochastic fixed point equations Next Article A comparative study of atomistic-based stress evaluation

A nonlinear fractional reaction-diffusion system applied to image denoising and decomposition

1.
Ecole Nationale des Sciences Appliquées de Marrakech, Université Cadi Ayyad, B.P. 575 Avenue Abdelkrim Al Khattabi Marrakech, Morocco
2.
Institut de Mathématiques de Bordeaux and INRIA-Carmen Bordeaux Sud-Ouest, Université de Bordeaux, 33076 Bordeaux Cedex, France
3.
Ecole Supérieure de Technologie d'Essaouira, Université Cadi Ayyad, B.P. 383 Essaouira El Jadida, Essaouira, Morocco

^* Corresponding author: Fahd Karami
^* Corresponding author: Fahd Karami

Received: April 30, 2020

Revised: June 30, 2020

Early access: November 2020

Published: September 2021

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Abstract

This paper is devoted to the mathematical and numerical study of a new proposed model based on a fractional diffusion equation coupled with a nonlinear regularization of the Total Variation operator. This model is primarily intended to introduce a weak norm in the fidelity term, where this norm is considered more appropriate for capturing very oscillatory characteristics interpreted as a texture. Furthermore, our proposed model profits from the benefits of a variable exponent used to distinguish the features of the image. By using Faedo-Galerkin method, we prove the well-posedness (existence and uniqueness) of the weak solution for the proposed model. Based on the alternating direction implicit method of Peaceman-Rachford and the approximations of the Gr$ \ddot{u} $nwald-Letnikov operators, we develop the numerical discretization of our fractional diffusion equation. Experimental results claim that our model provides high-quality results in cartoon-texture-edges decomposition and image denoising. In particular, our model can successfully reduce the staircase phenomenon during the image denoising. Furthermore, small details, texture and fine structures still maintained in the restored image. Finally, we compare our numerical results with the existing models in the literature.

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Mathematics Subject Classification: 26A33, 35K57, 94A08, 46E3x.

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Figure 1. Comparison between the regularization terms of the function $ f(x) = 1/|x| $ associated to the TV operator, which is weak in the numerical simulations, (the lines associated to this function are illustrated by the red curves in both figures: case $ \epsilon = 0 $ and case $ \gamma = 0 $). The first figure illustrates the curves obtained by the function $ f(x) = 1/\sqrt{|x|^{2}+\epsilon} $ and by different values of $ \epsilon $. The second figure illustrates the curves obtained by $ f(x) = ( \mathop{{\rm{log}}}(1+|x|))^{\gamma}/|x| $ and by different values of $ \gamma $. It is clear that the previously proposed converges rapidly to the regularization term associated with the TV operator from $ \gamma = 10^{-1} $. On the other hand, the other function converges from $ \epsilon = 10^{-4} $

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Figure 2. Original images

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Figure 3. The efficiency test of our model to denoise corrupted images with $ \sigma = 10 $ (see Table 1)

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Figure 4. The efficiency test of our model to denoise corrupted images with $ \sigma = 20 $ (see Table 2)

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Figure 5. The efficiency test of our model to denoise corrupted images with $ \sigma = 30 $ (see Table 3)

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Figure 6. Image denoising performed on the medical image ('Lung' image). Noise removal results provided by our model and other denoising techniques

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Figure 7. Image denoising performed on the grayscale image ('Einstein' image). Noise removal results provided by our model and other denoising techniques

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Figure 8. Image denoising performed on the grayscale image ('Lena' image). Noise removal results provided by our model and other denoising techniques

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Figure 10. The comparison results of different values of the fraction $ s $ obtained by our proposed model. The first row contains smooth images $ u $; The second row contains a residual part associated with $ u-f $

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Figure 9. This figure shows the ability to reduce the staircase effect between restored images with our proposed model and the TV model using the $ L^{2}- $norm from [36]. Our model can successfully reduce the staircase phenomenon during the image denoising

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Figure 11. Comparisons results between the classical TV model ($ L^{2}- $norm) from [36] and the AAKM model ($ H^{-1}- $norm) from [3] with our proposed model ($ H^{-0.5}- $norm)

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Figure 12. Results on Scan's image of line profile number $ 50 $. Red: original image; Green: cartoon part obtained by the TV model using the $ L^{2}- $norm [36]; Violet: cartoon part obtained by the AAKM model using the $ H^{-1}- $norm [3]; Blue: cartoon part obtained by our proposed model using the $ H^{-0.5}- $norm

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Figure 13. Comparison results of edge detector, contour lines and texture applied on a cartoon image obtained by the classical TV model (using the $ L^{2}- $norm) from [36] and AAKM model (using the $ H^{-1}- $norm) from [3] with our proposed model (using the $ H^{-0.5}- $norm)

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Figure 14. Results on Pepper's image of line profile number $ 50 $. Red: original image; Green: cartoon part obtained by the TV model using the $ L^{2}- $norm from [36]; Violet: cartoon part obtained by the AAKM model using the $ H^{-1}- $norm from [3]; Blue: cartoon part obtained by our proposed model using the $ H^{-0.5}- $norm

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Figure 15. Comparison results of edge detector, contour lines and texture applied on a cartoon image obtained by the classical TV model (the $ L^{2}- $norm) from [36] and the AAKM model (the $ H^{-1}- $norm) from [3] with our proposed model (the $ H^{-0.5}- $norm)

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Table 1. The SNR and PSNR values of Noisy ($ \sigma=10 $) and Restored images of the first efficiency test

Noise level	$\underline{Fig.3(A)}$	$\underline{Fig.3(D)}$	$\underline{Fig.3(B)}$	$\underline{Fig.3(E)}$	$\underline{Fig.3(C)}$	$\underline{Fig.3(F)}$
$ \sigma=10 $	$\mbox{Noisy}$	$\mbox{Restored}$	$\mbox{Noisy}$	$\mbox{Restored}$	$\mbox{Noisy}$	$\mbox{Restored}$
$\mbox{SNR}$	12.54	20.23	13.57	14.84	12.66	15.11
$\mbox{PSNR}$	28.14	35.82	27.22	29.16	28.17	30.60
$\mbox{SSIM}$	0.782	0.971	0.865	0.933	0.590	0.949

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Table 2. The $\mbox{SNR}$ and $\mbox{PSNR}$ values of Noisy ($ \sigma=20 $) and Restored images of the second efficiency test

$\mbox{Noise level}$	$\underline{Fig.4(A)}$	$\underline{Fig.4(D)}$	$\underline{Fig.4(B)}$	$\underline{Fig.4(E)}$	$\underline{Fig.4(C)}$	$\underline{Fig.4(F)}$
$ \sigma=20 $	$\mbox{Noisy}$	$\mbox{Restored}$	$\mbox{Noisy}$	$\mbox{Restored}$	$\mbox{Noisy}$	$\mbox{Restored}$
$\mbox{SNR}$	06.50	17.49	09.04	12.10	06.62	13.41
$\mbox{PSNR}$	22.10	33.09	22.02	25.38	22.13	28.90
$\mbox{SSIM}$	0.580	0.925	0.743	0.896	0.422	0.837

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Table 3. The $\mbox{SNR}$ and $\mbox{PSNR}$ values of Noisy ($ \sigma=30 $) and Restored images of the third efficiency test

$\mbox{Noise level}$	$\underline{Fig.5(A)}$	$\underline{Fig.5(D)}$	$\underline{Fig.5(B)}$	$\underline{Fig.5(E)}$	$\underline{Fig.5(C)}$	$\underline{Fig.5(F)}$
$ \sigma=30$	$\mbox{Noisy}$	$\mbox{Restored}$	$\mbox{Noisy}$	$\mbox{Restored}$	$\mbox{Noisy}$	$\mbox{Restored}$
$\mbox{SNR}$	03.01	16.12	05.51	10.07	03.06	12.44
$\mbox{PSNR}$	18.58	31.73	18.56	23.39	18.54	27.92
$\mbox{SSIM}$	0.501	0.884	0.650	0.885	0.325	0.780

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