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

Previous Article
Existence of strong solution for the Cauchy problem of fully compressible Navier-Stokes equations in two dimensions
DCDS-B Home
This Issue
Next Article
A nonuniform anisotropic FEM for elliptic boundary layer optimal control problems

doi: 10.3934/dcdsb.2020321

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

Abdelghafour Atlas ^1, , Mostafa Bendahmane ^2, , Fahd Karami ^3,, , Driss Meskine ^3, and Omar Oubbih ^3,

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

Received May 2020 Revised July 2020 Published November 2020

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.

Keywords: Image denoising, image decomposition, texture, TV operator, staircase effect, fractional calculus, nonlinear diffusion, exponent variable, Orlicz-space, Faedo-Galerkin method, Peaceman-Rachford ADI-method.

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

Citation: Abdelghafour Atlas, Mostafa Bendahmane, Fahd Karami, Driss Meskine, Omar Oubbih. A nonlinear fractional reaction-diffusion system applied to image denoising and decomposition. Discrete & Continuous Dynamical Systems - B, doi: 10.3934/dcdsb.2020321

References:

[1]	R. Aboulaich, D. Meskine and A. Souissi, New diffusion models in image processing, Comput. Math. Appl., 56 (2008), 874-882. doi: 10.1016/j.camwa.2008.01.017. Google Scholar
[2]	R. A. Adams, Sobolev Spaces, Ac. Press, New york, 1975. Google Scholar
[3]	L. Afraites, A. Atlas, F. Karami and D. Meskine, Some class of parabolic systems applied to image processing, Discrete Contin. Dyn. Syst. Ser. B, 21 (2016), 1671-1687. doi: 10.3934/dcdsb.2016017. Google Scholar
[4]	L. Alvarez, P.-L. Lions and J.-M. Morel, Image selective smoothing and edge detection by nonlinear diffusion. Ⅱ, SIAM J. Numer. Anal., 29 (1992), 845-866. doi: 10.1137/0729052. Google Scholar
[5]	F. Andreu, C. Ballester, V. Caselles and J. M. Mazón, Minimizing total variation flow, Differential Integral Equations, 14 (2001), 321-360. Google Scholar
[6]	N. Aronszajn, Boundary values of functions with finite Dirichlet integral, Techn. Report of Univ. of Kansas, 14 (1955), 77-94. Google Scholar
[7]	G. Aubert and J.-F. Aujol, Modeling very oscillating signals. Application to image processing, Appl. Math. Optim., 51 (2005), 163-182. doi: 10.1007/s00245-004-0812-z. Google Scholar
[8]	J.-F. Aujol, G. Aubert, L. Blanc-Féraud and A. Chambolle, Image decomposition into a bounded variation component and an oscillating component, J. Math. Imaging Vision, 22 (2005), 71-88. doi: 10.1007/s10851-005-4783-8. Google Scholar
[9]	A. Buades, B. Coll and J. M. Morel, A non-local algorithm for image denoising, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2 (2005), 60-65. Google Scholar
[10]	A. Chambolle, R. A. DeVore, N.-Y. Lee and B. J. Lucier, Nonlinear wavelet image processing: Variational problems, compression, and noise removal through wavelet shrinkage, IEEE Trans. Image Process., 7 (1998), 319–335. doi: 10.1109/83.661182. Google Scholar
[11]	E. A. Coddington and N. Levinson, Theory of ordinary differential equations, McGraw-Hill Book Company, Inc., New York-Toronto-London, (1955). Google Scholar
[12]	E. Di Nezza, G. Palatucci and E. Valdinoci, Hitchhiker's guide to the fractional Sobolev spaces, Bull. Sci. Math., 136 (2012), 521-573. doi: 10.1016/j.bulsci.2011.12.004. Google Scholar
[13]	K. Diethelm, The Analysis of Fractional Differential Equations, Lecture Notes in Mathematics, Springer-Verlag, Berlin, (2010). doi: 10.1007/978-3-642-14574-2. Google Scholar
[14]	S. Dipierro, X. Ros-Oton and E. Valdinoci, Nonlocal problems with Neumann boundary conditions, Rev. Mat. Iberoam., 33 (2017), 377-416. doi: 10.4171/RMI/942. Google Scholar
[15]	D. L. Donoho, De-noising by soft-thresholding, IEEE Transactions on Information Theory, 41 (1995), 613-627. doi: 10.1109/18.382009. Google Scholar
[16]	A. Elmahi and D. Meskine, Parabolic equations in Orlicz spaces, J. London Math. Soc. (2), 72 (2005), 410-428. doi: 10.1112/S0024610705006630. Google Scholar
[17]	A. Elmoataz, X. Desquesnes and O. Lézoray, Non-Local Morphological PDEs and $p$-Laplacian Equation on Graphs With Applications in Image Processing and Machine Learning, IEEE Journal of Selected Topics in Signal Processing, 6 (2012), 764-779. doi: 10.1109/JSTSP.2012.2216504. Google Scholar
[18]	E. Gagliardo, Ulteriori proprietà di alcune classi di funzioni in più variabili, Ricerche Mat., 8 (1959), 24-51. Google Scholar
[19]	J. B. Garnett, P. W. Jones, T. M. Le and L. A Vese, Modeling oscillatory components with the homogeneous spaces $B\dot MO^{-\alpha}$ and $\dot W{}^{-\alpha,p}$, Pure Appl. Math. Q., 7 (2011), 275-318. doi: 10.4310/PAMQ.2011.v7.n2.a2. Google Scholar
[20]	J. B. Garnett, T. M. Le, Y. Meyer and L. A Vese, Image decompositions using bounded variation and generalized homogeneous Besov spaces, Appl. Comput. Harmon. Anal., 23 (2007), 25-56. doi: 10.1016/j.acha.2007.01.005. Google Scholar
[21]	Y. Giga, M. Muszkieta and P. Rybka, A duality based approach to the minimizing total variation flow in the space $H^{-s}$, Jpn. J. Ind. Appl. Math., 36 (2019), 261-286. doi: 10.1007/s13160-018-00340-4. Google Scholar
[22]	G. Gilboa and S. Osher, Nonlocal linear image regularization and supervised segmentation, Multiscale Model. Simul., 6 (2007), 595-630. doi: 10.1137/060669358. Google Scholar
[23]	G. Gilboa and S. Osher, Nonlocal operators with applications to image processing, Multiscale Model. Simul., 7 (2008), 1005-1028. doi: 10.1137/070698592. Google Scholar
[24]	J.-P. Gossez, Nonlinear elliptic boundary value problems for equations with rapidly (or slowly) increasing coefficients, Trans. Amer. Math. Soc., 190 (1974), 163-205. doi: 10.1090/S0002-9947-1974-0342854-2. Google Scholar
[25]	Z. Guo, J. Yin and Q. Liu, On a reaction-diffusion system applied to image decomposition and restoration, Math. Comput. Modelling, 53 (2011), 1336-1350. doi: 10.1016/j.mcm.2010.12.031. Google Scholar
[26]	Y. Jin, J. Jost and G. Wang, A new nonlocal variational setting for image processing, Inverse Probl. Imaging, 9 (2015), 415-430. doi: 10.3934/ipi.2015.9.415. Google Scholar
[27]	Y. Kim and L. A. Vese, Image recovery using functions of bounded variation and Sobolev spaces of negative differentiability, Inverse Probl. Imaging, 3 (2009), 43-68. doi: 10.3934/ipi.2009.3.43. Google Scholar
[28]	S. Kindermann, S. Osher and P. W. Jones, Deblurring and denoising of images by nonlocal functionals, Multiscale Model. Simul., 4 (2005), 1091-1115. doi: 10.1137/050622249. Google Scholar
[29]	T. M. Le and L. A. Vese, Image decomposition using total variation and $ \rm{div} $($ \rm{BMO} $), Multiscale Model. Simul., 4 (2005), 390-423. doi: 10.1137/040610052. Google Scholar
[30]	L. H. Lieu and L. A. Vese, Image restoration and decomposition via bounded total variation and negative Hilbert-Sobolev spaces, Appl. Math. Optim., 58 (2008), 167-193. doi: 10.1007/s00245-008-9047-8. Google Scholar
[31]	X. Liu and L. Huang, A new nonlocal total variation regularization algorithm for image denoising, Math. Comput. Simulation, 97 (2014), 224-233. doi: 10.1016/j.matcom.2013.10.001. Google Scholar
[32]	Y. Meyer, Oscillating Patterns in Image Processing and Nonlinear Evolution Equations, American Mathematical Society, Providence, RI, (2001). doi: 10.1090/ulect/022. Google Scholar
[33]	K. S. Miller and B. Ross, An Introduction to the Fractional Calculus and Fractional Differential Equations, John Wiley & Sons, Inc., New York, (1993). Google Scholar
[34]	S. Osher, A. Solé and L. Vese, Image decomposition and restoration using total variation minimization and the $H^{-1}$ norm, Multiscale Model. Simul., 1 (2003), 349-370. doi: 10.1137/S1540345902416247. Google Scholar
[35]	P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (1990), 629-639. doi: 10.1109/34.56205. Google Scholar
[36]	L. I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms, Phys. D., 60 (1992), 259-268. doi: 10.1016/0167-2789(92)90242-F. Google Scholar
[37]	L. N. Slobodeckiĭ, Generalized Sobolev spaces and their application to boundary problems for partial differential equations, Leningrad. Gos. Ped. Inst. Učen. Zap., 197 (1958), 54-112. Google Scholar
[38]	N. N. Tarkhanov, The Analysis of Solutions of Elliptic Equations, Kluwer Academic Publishers Group, Dordrecht, (1997). doi: 10.1007/978-94-015-8804-1. Google Scholar
[39]	L. A. Vese and S. J. Osher, Modeling textures with total variation minimization and oscillating patterns in image processing, J. Sci. Comput., 19 (2003), 553-572. doi: 10.1023/A:1025384832106. Google Scholar
[40]	Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing, 13 (2004), 600-612. doi: 10.1109/TIP.2003.819861. Google Scholar
[41]	L. P. Yaroslavsky, Digital Picture Processing, Springer-Verlag, Berlin, 1985. doi: 10.1007/978-3-642-81929-2. Google Scholar

show all references

References:

[1]	R. Aboulaich, D. Meskine and A. Souissi, New diffusion models in image processing, Comput. Math. Appl., 56 (2008), 874-882. doi: 10.1016/j.camwa.2008.01.017. Google Scholar
[2]	R. A. Adams, Sobolev Spaces, Ac. Press, New york, 1975. Google Scholar
[3]	L. Afraites, A. Atlas, F. Karami and D. Meskine, Some class of parabolic systems applied to image processing, Discrete Contin. Dyn. Syst. Ser. B, 21 (2016), 1671-1687. doi: 10.3934/dcdsb.2016017. Google Scholar
[4]	L. Alvarez, P.-L. Lions and J.-M. Morel, Image selective smoothing and edge detection by nonlinear diffusion. Ⅱ, SIAM J. Numer. Anal., 29 (1992), 845-866. doi: 10.1137/0729052. Google Scholar
[5]	F. Andreu, C. Ballester, V. Caselles and J. M. Mazón, Minimizing total variation flow, Differential Integral Equations, 14 (2001), 321-360. Google Scholar
[6]	N. Aronszajn, Boundary values of functions with finite Dirichlet integral, Techn. Report of Univ. of Kansas, 14 (1955), 77-94. Google Scholar
[7]	G. Aubert and J.-F. Aujol, Modeling very oscillating signals. Application to image processing, Appl. Math. Optim., 51 (2005), 163-182. doi: 10.1007/s00245-004-0812-z. Google Scholar
[8]	J.-F. Aujol, G. Aubert, L. Blanc-Féraud and A. Chambolle, Image decomposition into a bounded variation component and an oscillating component, J. Math. Imaging Vision, 22 (2005), 71-88. doi: 10.1007/s10851-005-4783-8. Google Scholar
[9]	A. Buades, B. Coll and J. M. Morel, A non-local algorithm for image denoising, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2 (2005), 60-65. Google Scholar
[10]	A. Chambolle, R. A. DeVore, N.-Y. Lee and B. J. Lucier, Nonlinear wavelet image processing: Variational problems, compression, and noise removal through wavelet shrinkage, IEEE Trans. Image Process., 7 (1998), 319–335. doi: 10.1109/83.661182. Google Scholar
[11]	E. A. Coddington and N. Levinson, Theory of ordinary differential equations, McGraw-Hill Book Company, Inc., New York-Toronto-London, (1955). Google Scholar
[12]	E. Di Nezza, G. Palatucci and E. Valdinoci, Hitchhiker's guide to the fractional Sobolev spaces, Bull. Sci. Math., 136 (2012), 521-573. doi: 10.1016/j.bulsci.2011.12.004. Google Scholar
[13]	K. Diethelm, The Analysis of Fractional Differential Equations, Lecture Notes in Mathematics, Springer-Verlag, Berlin, (2010). doi: 10.1007/978-3-642-14574-2. Google Scholar
[14]	S. Dipierro, X. Ros-Oton and E. Valdinoci, Nonlocal problems with Neumann boundary conditions, Rev. Mat. Iberoam., 33 (2017), 377-416. doi: 10.4171/RMI/942. Google Scholar
[15]	D. L. Donoho, De-noising by soft-thresholding, IEEE Transactions on Information Theory, 41 (1995), 613-627. doi: 10.1109/18.382009. Google Scholar
[16]	A. Elmahi and D. Meskine, Parabolic equations in Orlicz spaces, J. London Math. Soc. (2), 72 (2005), 410-428. doi: 10.1112/S0024610705006630. Google Scholar
[17]	A. Elmoataz, X. Desquesnes and O. Lézoray, Non-Local Morphological PDEs and $p$-Laplacian Equation on Graphs With Applications in Image Processing and Machine Learning, IEEE Journal of Selected Topics in Signal Processing, 6 (2012), 764-779. doi: 10.1109/JSTSP.2012.2216504. Google Scholar
[18]	E. Gagliardo, Ulteriori proprietà di alcune classi di funzioni in più variabili, Ricerche Mat., 8 (1959), 24-51. Google Scholar
[19]	J. B. Garnett, P. W. Jones, T. M. Le and L. A Vese, Modeling oscillatory components with the homogeneous spaces $B\dot MO^{-\alpha}$ and $\dot W{}^{-\alpha,p}$, Pure Appl. Math. Q., 7 (2011), 275-318. doi: 10.4310/PAMQ.2011.v7.n2.a2. Google Scholar
[20]	J. B. Garnett, T. M. Le, Y. Meyer and L. A Vese, Image decompositions using bounded variation and generalized homogeneous Besov spaces, Appl. Comput. Harmon. Anal., 23 (2007), 25-56. doi: 10.1016/j.acha.2007.01.005. Google Scholar
[21]	Y. Giga, M. Muszkieta and P. Rybka, A duality based approach to the minimizing total variation flow in the space $H^{-s}$, Jpn. J. Ind. Appl. Math., 36 (2019), 261-286. doi: 10.1007/s13160-018-00340-4. Google Scholar
[22]	G. Gilboa and S. Osher, Nonlocal linear image regularization and supervised segmentation, Multiscale Model. Simul., 6 (2007), 595-630. doi: 10.1137/060669358. Google Scholar
[23]	G. Gilboa and S. Osher, Nonlocal operators with applications to image processing, Multiscale Model. Simul., 7 (2008), 1005-1028. doi: 10.1137/070698592. Google Scholar
[24]	J.-P. Gossez, Nonlinear elliptic boundary value problems for equations with rapidly (or slowly) increasing coefficients, Trans. Amer. Math. Soc., 190 (1974), 163-205. doi: 10.1090/S0002-9947-1974-0342854-2. Google Scholar
[25]	Z. Guo, J. Yin and Q. Liu, On a reaction-diffusion system applied to image decomposition and restoration, Math. Comput. Modelling, 53 (2011), 1336-1350. doi: 10.1016/j.mcm.2010.12.031. Google Scholar
[26]	Y. Jin, J. Jost and G. Wang, A new nonlocal variational setting for image processing, Inverse Probl. Imaging, 9 (2015), 415-430. doi: 10.3934/ipi.2015.9.415. Google Scholar
[27]	Y. Kim and L. A. Vese, Image recovery using functions of bounded variation and Sobolev spaces of negative differentiability, Inverse Probl. Imaging, 3 (2009), 43-68. doi: 10.3934/ipi.2009.3.43. Google Scholar
[28]	S. Kindermann, S. Osher and P. W. Jones, Deblurring and denoising of images by nonlocal functionals, Multiscale Model. Simul., 4 (2005), 1091-1115. doi: 10.1137/050622249. Google Scholar
[29]	T. M. Le and L. A. Vese, Image decomposition using total variation and $ \rm{div} $($ \rm{BMO} $), Multiscale Model. Simul., 4 (2005), 390-423. doi: 10.1137/040610052. Google Scholar
[30]	L. H. Lieu and L. A. Vese, Image restoration and decomposition via bounded total variation and negative Hilbert-Sobolev spaces, Appl. Math. Optim., 58 (2008), 167-193. doi: 10.1007/s00245-008-9047-8. Google Scholar
[31]	X. Liu and L. Huang, A new nonlocal total variation regularization algorithm for image denoising, Math. Comput. Simulation, 97 (2014), 224-233. doi: 10.1016/j.matcom.2013.10.001. Google Scholar
[32]	Y. Meyer, Oscillating Patterns in Image Processing and Nonlinear Evolution Equations, American Mathematical Society, Providence, RI, (2001). doi: 10.1090/ulect/022. Google Scholar
[33]	K. S. Miller and B. Ross, An Introduction to the Fractional Calculus and Fractional Differential Equations, John Wiley & Sons, Inc., New York, (1993). Google Scholar
[34]	S. Osher, A. Solé and L. Vese, Image decomposition and restoration using total variation minimization and the $H^{-1}$ norm, Multiscale Model. Simul., 1 (2003), 349-370. doi: 10.1137/S1540345902416247. Google Scholar
[35]	P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (1990), 629-639. doi: 10.1109/34.56205. Google Scholar
[36]	L. I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms, Phys. D., 60 (1992), 259-268. doi: 10.1016/0167-2789(92)90242-F. Google Scholar
[37]	L. N. Slobodeckiĭ, Generalized Sobolev spaces and their application to boundary problems for partial differential equations, Leningrad. Gos. Ped. Inst. Učen. Zap., 197 (1958), 54-112. Google Scholar
[38]	N. N. Tarkhanov, The Analysis of Solutions of Elliptic Equations, Kluwer Academic Publishers Group, Dordrecht, (1997). doi: 10.1007/978-94-015-8804-1. Google Scholar
[39]	L. A. Vese and S. J. Osher, Modeling textures with total variation minimization and oscillating patterns in image processing, J. Sci. Comput., 19 (2003), 553-572. doi: 10.1023/A:1025384832106. Google Scholar
[40]	Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing, 13 (2004), 600-612. doi: 10.1109/TIP.2003.819861. Google Scholar
[41]	L. P. Yaroslavsky, Digital Picture Processing, Springer-Verlag, Berlin, 1985. doi: 10.1007/978-3-642-81929-2. Google Scholar

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} $

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

$\mbox{Noise level}$	$\underline{Fig.4(A)}$	$\underline{Fig.4(D)}$	$\underline{Fig.4(B)}$	$\underline{Fig.4(E)}$	$\underline{Fig.4(C)}$	$\underline{Fig.4(F)}$
$ \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

$\mbox{Noise level}$	$\underline{Fig.5(A)}$	$\underline{Fig.5(D)}$	$\underline{Fig.5(B)}$	$\underline{Fig.5(E)}$	$\underline{Fig.5(C)}$	$\underline{Fig.5(F)}$
$ \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

[1]	Mehdi Bastani, Davod Khojasteh Salkuyeh. On the GSOR iteration method for image restoration. Numerical Algebra, Control & Optimization, 2021, 11 (1) : 27-43. doi: 10.3934/naco.2020013
[2]	Leilei Wei, Yinnian He. A fully discrete local discontinuous Galerkin method with the generalized numerical flux to solve the tempered fractional reaction-diffusion equation. Discrete & Continuous Dynamical Systems - B, 2020 doi: 10.3934/dcdsb.2020319
[3]	Zexuan Liu, Zhiyuan Sun, Jerry Zhijian Yang. A numerical study of superconvergence of the discontinuous Galerkin method by patch reconstruction. Electronic Research Archive, 2020, 28 (4) : 1487-1501. doi: 10.3934/era.2020078
[4]	Yue Feng, Yujie Liu, Ruishu Wang, Shangyou Zhang. A conforming discontinuous Galerkin finite element method on rectangular partitions. Electronic Research Archive, , () : -. doi: 10.3934/era.2020120
[5]	Mostafa Mbekhta. Representation and approximation of the polar factor of an operator on a Hilbert space. Discrete & Continuous Dynamical Systems - S, 2020 doi: 10.3934/dcdss.2020463
[6]	Yuxia Guo, Shaolong Peng. A direct method of moving planes for fully nonlinear nonlocal operators and applications. Discrete & Continuous Dynamical Systems - S, 2020 doi: 10.3934/dcdss.2020462
[7]	Jia Cai, Guanglong Xu, Zhensheng Hu. Sketch-based image retrieval via CAT loss with elastic net regularization. Mathematical Foundations of Computing, 2020, 3 (4) : 219-227. doi: 10.3934/mfc.2020013
[8]	Zuliang Lu, Fei Huang, Xiankui Wu, Lin Li, Shang Liu. Convergence and quasi-optimality of $ L^2- $norms based an adaptive finite element method for nonlinear optimal control problems. Electronic Research Archive, 2020, 28 (4) : 1459-1486. doi: 10.3934/era.2020077
[9]	Hong Niu, Zhijiang Feng, Qijin Xiao, Yajun Zhang. A PID control method based on optimal control strategy. Numerical Algebra, Control & Optimization, 2021, 11 (1) : 117-126. doi: 10.3934/naco.2020019
[10]	Zedong Yang, Guotao Wang, Ravi P. Agarwal, Haiyong Xu. Existence and nonexistence of entire positive radial solutions for a class of Schrödinger elliptic systems involving a nonlinear operator. Discrete & Continuous Dynamical Systems - S, 2020 doi: 10.3934/dcdss.2020436
[11]	Li-Bin Liu, Ying Liang, Jian Zhang, Xiaobing Bao. A robust adaptive grid method for singularly perturbed Burger-Huxley equations. Electronic Research Archive, 2020, 28 (4) : 1439-1457. doi: 10.3934/era.2020076
[12]	Noah Stevenson, Ian Tice. A truncated real interpolation method and characterizations of screened Sobolev spaces. Communications on Pure & Applied Analysis, 2020, 19 (12) : 5509-5566. doi: 10.3934/cpaa.2020250
[13]	S. Sadeghi, H. Jafari, S. Nemati. Solving fractional Advection-diffusion equation using Genocchi operational matrix based on Atangana-Baleanu derivative. Discrete & Continuous Dynamical Systems - S, 2020 doi: 10.3934/dcdss.2020435
[14]	Marion Darbas, Jérémy Heleine, Stephanie Lohrengel. Numerical resolution by the quasi-reversibility method of a data completion problem for Maxwell's equations. Inverse Problems & Imaging, 2020, 14 (6) : 1107-1133. doi: 10.3934/ipi.2020056
[15]	Gang Bao, Mingming Zhang, Bin Hu, Peijun Li. An adaptive finite element DtN method for the three-dimensional acoustic scattering problem. Discrete & Continuous Dynamical Systems - B, 2020 doi: 10.3934/dcdsb.2020351
[16]	Qingfang Wang, Hua Yang. Solutions of nonlocal problem with critical exponent. Communications on Pure & Applied Analysis, 2020, 19 (12) : 5591-5608. doi: 10.3934/cpaa.2020253
[17]	Maoding Zhen, Binlin Zhang, Vicenţiu D. Rădulescu. Normalized solutions for nonlinear coupled fractional systems: Low and high perturbations in the attractive case. Discrete & Continuous Dynamical Systems - A, 2020 doi: 10.3934/dcds.2020379
[18]	Omid Nikan, Seyedeh Mahboubeh Molavi-Arabshai, Hossein Jafari. Numerical simulation of the nonlinear fractional regularized long-wave model arising in ion acoustic plasma waves. Discrete & Continuous Dynamical Systems - S, 2020 doi: 10.3934/dcdss.2020466
[19]	Wei Feng, Michael Freeze, Xin Lu. On competition models under allee effect: Asymptotic behavior and traveling waves. Communications on Pure & Applied Analysis, 2020, 19 (12) : 5609-5626. doi: 10.3934/cpaa.2020256
[20]	Hirokazu Ninomiya. Entire solutions of the Allen–Cahn–Nagumo equation in a multi-dimensional space. Discrete & Continuous Dynamical Systems - A, 2021, 41 (1) : 395-412. doi: 10.3934/dcds.2020364

American Institute of Mathematical Sciences

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

References:

References:

Tools

Metrics

Other articlesby authors

Tools

Tools

Tools

Metrics

Other articlesby authors

Other articles
by authors

Other articles
by authors