Yıl 2023,
Cilt: 5 Sayı: 4, 300 - 307, 31.12.2023
Alka Chauhan
,
Santosh Kumar
,
Khursheed Alam
Kaynakça
- Álvarez, L., P.-L. Lions, and J.-M. Morel, 1992 Image selective
smoothing and edge detection by nonlinear diffusion. ii. SIAM
Journal on Numerical Analysis 29: 845–866.
- Barcelos, C., M. Boaventura, and E. Silva, 2003 A well-balanced
flow equation for noise removal and edge detection. IEEE Transactions
on Image Processing 12: 751–763.
- Barcelos, C. A. Z., M. Boaventura, and E. C. Silva, 2005 Edge detection
and noise removal by use of a partial differential equation
with automatic selection of parameters. Computational & Applied
Mathematics 24: 131–150.
- Brezis, H., 1987 Analyse Fonctionnelle: Theorie et Applications. Masson,
Paris, 1987 (2e Tirage).
- Catté, F., P. Lions, J. Morel, and T. Coll, 1992 Image selective
smoothing and edge detection by nonlinear diffusion*. SIAM J.
Numer. Anal. 29: 182–193.
- Chan, T. F., G. H. Golub, and P. Mulet, 1999 A nonlinear primaldual
method for total variation-based image restoration. SIAM J.
Sci. Comput. 20: 1964–1977.
- Chang, Q. and I.-L. Chern, 2003 Acceleration methods for total
variation-based image denoising. SIAM Journal on Scientific
Computing 25: 982–994.
- Charbonnier, P., L. Blanc-Féraud, G. Aubert, and M. Barlaud, 1994
Two deterministic half-quadratic regularization algorithms for
computed imaging. Proceedings of 1st International Conference
on Image Processing 2: 168–172 vol.2.
- Chen, Y.-G., Y. Giga, and S. Goto, 1999 pp. 375–412 in Uniqueness
and Existence of Viscosity Solutions of Generalized mean Curvature
Flow Equations, edited by Ball, J. M., D. Kinderlehrer, P. Podio-
Guidugli, and M. Slemrod, Springer Berlin Heidelberg.
- El-Fallah, A. I. and G. E. Ford, 1998 On mean curvature diffusion
in nonlinear image filtering. Pattern Recognition Letters 19: 433–
437.
- El-Shorbagy, M. A., M. u. Rahman, and Y. Karaca, 2023 A computational
analysis fractional complex-order values by abc operator
and mittag-leffler kernel modeling. Fractals 0: null.
- Gilboa, G., N. Sochen, and Y. Zeevi, 2006 Variational denoising of
partly textured images by spatially varying constraints. IEEE
Transactions on Image Processing 15: 2281–2289.
- Haidong, Q., M. ur Rahman, S. E. Al Hazmi, M. F. Yassen,
S. Salahshour, et al., 2023 Analysis of non-equilibrium 4d dynamical
system with fractal fractional mittag-leffler kernel. Engineering
Science and Technology, an International Journal 37:
101319.
- Kumar, S. and K. Alam, 2021a A new class of nonlinear hyperbolicparabolic
model for image denoising with forward-backward
diffusivity. Mathematics in Engineering, Science & Aerospace
12: 435–441.
- Kumar, S. and K. Alam, 2021b Pde-based hyperbolic-parabolic
model for image denoising with forward-backward diffusivity.
Computational Methods for Differential Equations 9: 1100–1108.
- Lapidus, L. and G. F. Pinder, 1983 Numerical solution of partial
differential equations in science and engineering. SIAM Review
25: 581–582.
- Li, M.-M. and B.-Z. Li, 2021 A novel weighted total variation model
for image denoising. IET Image Processing 15: 2749–2760.
- Marquina, A. and S. Osher, 2000 Explicit algorithms for a new time
dependent model based on level set motion for nonlinear deblurring
and noise removal. SIAM Journal on Scientific Computing
22: 387–405.
- Phan, T. D. K., 2020 A weighted total variation based image denoising
model using mean curvature. Optik 217: 164940.
- Prasath, V. B. S. and D. Vorotnikov, 2014 Weighted and wellbalanced
anisotropic diffusion scheme for image denoising and
restoration. Nonlinear Analysis-real World Applications 17: 33–
46.
- Qu, H.-D., X. Liu, X. Lu, M. ur Rahman, and Z.-H. She, 2022 Neural
network method for solving nonlinear fractional advectiondiffusion
equation with spatiotemporal variable-order. Chaos,
Solitons & Fractals 156: 111856.
- Rudin, L. I., S. Osher, and E. Fatemi, 1992 Nonlinear total variation
based noise removal algorithms. Physica D: Nonlinear
Phenomena 60: 259–268.
- Smolka, B., 2008 Modified biased anisotropic diffusion processing
of noisy color images. In 2008 9th International Conference on
Signal Processing, pp. 777–780.
- Vogel, C. R. and M. E. Oman, 1996 Iterative methods for total
variation denoising. SIAM Journal on Scientific Computing 17:
227–238.
- Weickert, J., 1997 A review of nonlinear diffusion filtering. In Scale-
Space Theory in Computer Vision, edited by B. ter Haar Romeny,
L. Florack, J. Koenderink, and M. Viergever, pp. 1–28, Berlin,
Heidelberg, Springer Berlin Heidelberg.
- XU, C., M. UR RAHMAN, B. FATIMA, and Y. KARACA, 2022
Theoretical and numerical investigation of complexities in
fractional-order chaotic system having torus attractors. Fractals
30: 2250164.
Weighted and Well-Balanced Nonlinear TV-Based Time-Dependent Model for Image Denoising
Yıl 2023,
Cilt: 5 Sayı: 4, 300 - 307, 31.12.2023
Alka Chauhan
,
Santosh Kumar
,
Khursheed Alam
Öz
The partial differential equation (PDE)-based models are widely used to remove additive Gaussian white noise and preserve edges, and one of the most widely used methods is the total variation denoising algorithm. Total variation (TV) denoising algorithm-based time-dependent models have seen considerable success in the field of image-denoising and edge detection. TV denoising algorithm is based on that signals with spurious detail have a high total variation and reduction of unwanted signals to achieve noise-free images. It is a constrained optimization-type algorithm. The Lagrange multiplier and gradient descent method are used to solve the TV algorithm to reach the PDE-based time dependent model. To eliminate additive noise and preserve edges, we investigate a class of weighted time-dependent model in this study. The proposed method is investigated in a well-balanced flow form that extends the time-dependent model with an adaptive fidelity element. Adaptive function is fusing into the regularization term of the classical time-dependent model which successfully enhances the intensity of the regularizer function. We maintain the ability of the time-dependent model without any oscillation effects. Furthermore, we want to prove the viscosity solution of our weighted and well balanced time-dependent model, demonstrating its existence and uniqueness. The finite difference method is applied to discretize the nonlinear time-dependent models. The numerical results are expressed as a statistic known as the peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). Numerical experiments demonstrate that the proposed model yields good performance compared with the previous time-dependent model.
Kaynakça
- Álvarez, L., P.-L. Lions, and J.-M. Morel, 1992 Image selective
smoothing and edge detection by nonlinear diffusion. ii. SIAM
Journal on Numerical Analysis 29: 845–866.
- Barcelos, C., M. Boaventura, and E. Silva, 2003 A well-balanced
flow equation for noise removal and edge detection. IEEE Transactions
on Image Processing 12: 751–763.
- Barcelos, C. A. Z., M. Boaventura, and E. C. Silva, 2005 Edge detection
and noise removal by use of a partial differential equation
with automatic selection of parameters. Computational & Applied
Mathematics 24: 131–150.
- Brezis, H., 1987 Analyse Fonctionnelle: Theorie et Applications. Masson,
Paris, 1987 (2e Tirage).
- Catté, F., P. Lions, J. Morel, and T. Coll, 1992 Image selective
smoothing and edge detection by nonlinear diffusion*. SIAM J.
Numer. Anal. 29: 182–193.
- Chan, T. F., G. H. Golub, and P. Mulet, 1999 A nonlinear primaldual
method for total variation-based image restoration. SIAM J.
Sci. Comput. 20: 1964–1977.
- Chang, Q. and I.-L. Chern, 2003 Acceleration methods for total
variation-based image denoising. SIAM Journal on Scientific
Computing 25: 982–994.
- Charbonnier, P., L. Blanc-Féraud, G. Aubert, and M. Barlaud, 1994
Two deterministic half-quadratic regularization algorithms for
computed imaging. Proceedings of 1st International Conference
on Image Processing 2: 168–172 vol.2.
- Chen, Y.-G., Y. Giga, and S. Goto, 1999 pp. 375–412 in Uniqueness
and Existence of Viscosity Solutions of Generalized mean Curvature
Flow Equations, edited by Ball, J. M., D. Kinderlehrer, P. Podio-
Guidugli, and M. Slemrod, Springer Berlin Heidelberg.
- El-Fallah, A. I. and G. E. Ford, 1998 On mean curvature diffusion
in nonlinear image filtering. Pattern Recognition Letters 19: 433–
437.
- El-Shorbagy, M. A., M. u. Rahman, and Y. Karaca, 2023 A computational
analysis fractional complex-order values by abc operator
and mittag-leffler kernel modeling. Fractals 0: null.
- Gilboa, G., N. Sochen, and Y. Zeevi, 2006 Variational denoising of
partly textured images by spatially varying constraints. IEEE
Transactions on Image Processing 15: 2281–2289.
- Haidong, Q., M. ur Rahman, S. E. Al Hazmi, M. F. Yassen,
S. Salahshour, et al., 2023 Analysis of non-equilibrium 4d dynamical
system with fractal fractional mittag-leffler kernel. Engineering
Science and Technology, an International Journal 37:
101319.
- Kumar, S. and K. Alam, 2021a A new class of nonlinear hyperbolicparabolic
model for image denoising with forward-backward
diffusivity. Mathematics in Engineering, Science & Aerospace
12: 435–441.
- Kumar, S. and K. Alam, 2021b Pde-based hyperbolic-parabolic
model for image denoising with forward-backward diffusivity.
Computational Methods for Differential Equations 9: 1100–1108.
- Lapidus, L. and G. F. Pinder, 1983 Numerical solution of partial
differential equations in science and engineering. SIAM Review
25: 581–582.
- Li, M.-M. and B.-Z. Li, 2021 A novel weighted total variation model
for image denoising. IET Image Processing 15: 2749–2760.
- Marquina, A. and S. Osher, 2000 Explicit algorithms for a new time
dependent model based on level set motion for nonlinear deblurring
and noise removal. SIAM Journal on Scientific Computing
22: 387–405.
- Phan, T. D. K., 2020 A weighted total variation based image denoising
model using mean curvature. Optik 217: 164940.
- Prasath, V. B. S. and D. Vorotnikov, 2014 Weighted and wellbalanced
anisotropic diffusion scheme for image denoising and
restoration. Nonlinear Analysis-real World Applications 17: 33–
46.
- Qu, H.-D., X. Liu, X. Lu, M. ur Rahman, and Z.-H. She, 2022 Neural
network method for solving nonlinear fractional advectiondiffusion
equation with spatiotemporal variable-order. Chaos,
Solitons & Fractals 156: 111856.
- Rudin, L. I., S. Osher, and E. Fatemi, 1992 Nonlinear total variation
based noise removal algorithms. Physica D: Nonlinear
Phenomena 60: 259–268.
- Smolka, B., 2008 Modified biased anisotropic diffusion processing
of noisy color images. In 2008 9th International Conference on
Signal Processing, pp. 777–780.
- Vogel, C. R. and M. E. Oman, 1996 Iterative methods for total
variation denoising. SIAM Journal on Scientific Computing 17:
227–238.
- Weickert, J., 1997 A review of nonlinear diffusion filtering. In Scale-
Space Theory in Computer Vision, edited by B. ter Haar Romeny,
L. Florack, J. Koenderink, and M. Viergever, pp. 1–28, Berlin,
Heidelberg, Springer Berlin Heidelberg.
- XU, C., M. UR RAHMAN, B. FATIMA, and Y. KARACA, 2022
Theoretical and numerical investigation of complexities in
fractional-order chaotic system having torus attractors. Fractals
30: 2250164.