Volume 20, Issue 1 (4-2025)
IJMSI 2025, 20(1): 53-67 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Gatcha M, Messelmi F, Saadi S. Image Enhancement and Restoration Approach Based on Anisotropic Diffusion. IJMSI 2025; 20 (1) :53-67
URL: http://ijmsi.ir/article-1-1772-en.html
URL: http://ijmsi.ir/article-1-1772-en.html
Abstract:
We propose a new approach for image enhancement, denoising and restoration, using an anisotropic diffusion based on P-M model and L.V and al. equation, replacing the gradient by motion by mean curvature to detect noise direction for each degraded pixel locally, applying the gradient in Gaussian kernel term to restore the degraded pixels and adding a time term supporting the restoration process. For execution progress, the numerical discretization for the terms of PDE modeling (obtained by the approximation by difference finite volumes finite method, Taylor method and Simpsons improved method), concludes an algorithm treats noised image regardless the noise type (salt-pepper or Gaussian or speckle) better than other filters based whether on anisotropic diffusion or total, shown in the experimental results (using MATLAB program), and demonstrated through PSNR and SSIM.
Type of Study: Research paper |
Subject:
General
References
1. L. Alvarez, PL. Lions, JM. Morel, Image Selective Smoothing and Edge Detection by Nonlinear Diffusion, SIAM J. NUMER, 29(3), (1992), 845-866. [DOI:10.1137/0729052]
2. T. Barbu, A. Miranville, C. Moro, A Qualitative Analysis and Numerical Simulations of a Nonlinear Second-order Anisotropic Diffusion Problem with Non-homogeneous Cauchy-Neumann Boundary Conditions, Applied Mathematics and Computation, 350, (2019), 170-180. [DOI:10.1016/j.amc.2019.01.004]
3. A. Boucher, Image Processing-spatial Convolution-, www.etud.iro, 2016.
4. V. Choqueuse, Convolution Product-principe and Propieties, Westerner Britann, 2016.
5. E. Cuervas, H. Becerra, A. Luque, Anisotropic Diffusion Filtering Through Multiobjective Optimization, Mathematics and Computers in Simulation, 181, (2020), 410-429. [DOI:10.1016/j.matcom.2020.09.030]
6. M. Gatcha, F. Messelmi, S. Saadi, An Anisotropic Diffusion Adaptive Filter for Image Denoising and Restoration Applied on Satellite Remote Sensing Images: a Case Study, Engineering Technology & Applied Science Research, 12(6), (2022), 9715-9719. [DOI:10.48084/etasr.5363]
7. S. Godounov, V. Riabenki, Differences Schemes (Introduction of the Theory), University publication offce, Algeria, 1987.
8. S. A. Halim, N. N. Wira, Variations of Diffusion Functions on Perona and Malik Model for Noise Removal, Fourth-order Partial Differ, AIP Conference Proceedings, 2266(1), (2020), 1-8.
9. J. J. Koenderink, The Structure of Images, Biological Cybernetics, 50, (1984), 363-370. [DOI:10.1007/BF00336961]
10. A. Lanza, S. Morigi, IW. Selesnick, F. Sgallari, Sparsity-inducing Nonconvex Non Separable Regularization for Convex Image, Society for Industrial and Applied Mathematics, 12(2), (2019), 1099-1134. [DOI:10.1137/18M1199149]
11. D. Marr, E. Hildreth, Theory of Edge detection, Proceedings of the Royal Society Series B Biological Sciences, 207(1167), (1980), 187-217. [DOI:10.1098/rspb.1980.0020]
12. R. R. Nair, E. David, S. Rajagopal, A Robust Anisotropic Diffusion Filter with Low Arithmetic Complexity for Images, EURASIP Journal on Image and Video Processing, 48, (2019). [DOI:10.1186/s13640-019-0444-5]
13. P. Perona, J. Malik, Scale-space and Edge Detection Using Anisotropic Diffusion, IEEE Transactions On Pattern Analysis And Machine Intelligence, 12(7), (1990), 629-639. [DOI:10.1109/34.56205]
14. J. C. Russ,The Image Processing Cookbook, 4th edition, USA, 2017.
15. N. Thakur, N. U. Khan, S. D. Sharma, A Two Phase Ultrasound Image De-speckling Framework by Nonlocal Means on Anisotropic Diffused Image Data, Informatica, 47(2), (2023), 221-234. [DOI:10.31449/inf.v47i2.4378]
16. A. Theljani, Z. Belhachmi, M. Moakher, High-order Anisotropic Diffusion Operators in Spaces of Variable Exponents and Application to Image Inpainting and Restoration Problems, Nonlinear Analysis Real World Applications, 47, (2019), 251-271. [DOI:10.1016/j.nonrwa.2018.10.013]
17. J. Xu, Y. Hao, M. Li, X. Zhang, A Novel Variational Model for Image Decomposition, Signal and Image and Video Processing, 13, (2019), 967-974. [DOI:10.1007/s11760-019-01434-3]
18. H. Zeng, X. Xie, J. Ning, Hyperspectral Image Denoising via Global Spatial-spectral Total Variation Regularized Nonconvex Local Low-rank Tensor Approximation, Signal Processing, 178, (2021). [DOI:10.1016/j.sigpro.2020.107805]
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
