Image Driven Isotropic Diffusivity and Complementary Regularization Approach for Image Denoising Problem
of the energy functional leads to the Partial Differential equation (PDE)-based problem. We are looking for a steady state solution of equivalent time dependent problem. We discretize the problem with standard ﬁnite differences. The steady-state numerical solution of the time dependent problem leads to the iterative procedure, which allow to compute a regularized version of the solution as a denoised image. We have applied our designed model on synthetic as well as real images. The numerous experiments have been conducted to analyse the performance of the method for the different choices of scaling parameters. From the quality of the obtained results and comparative study it is observed that the proposed model performs well as compared to well existing methods.
Amur, K. B. , ‘A posteriori control of regularization for complementary image motion problem’, Sindh University Research Journal-SURJ (Science Series) 45(3).
Amur, M. , ‘adaptive numerical regularization for variational model for denoising with complementry approach’, MS Thesis, QUEST Nawabshah .
Barbu, T., Marinoschi, G., Morosanu, C. and Munteanu, I. , ‘Advances in variational and partial differential equation-based models for image processing and computer vision’.
Beck, A. and Teboulle, M. , ‘Fast gradient-based algorithms for constrained total variation image
denoising and deblurring problems’, IEEE transactions on image processing 18(11), 2419–2434.
Benesty, J., Chen, J. and Huang, Y. , Study of the widely linear wiener ﬁlter for noise reduction, in
‘2010 IEEE International Conference on Acoustics, Speech and Signal Processing’, IEEE, pp. 205–208.
Bharati, S., Khan, T. Z., Podder, P. and Hung, N. Q. , A comparative analysis of image denoising
problem: noise models, denoising ﬁlters and applications, in ‘Cognitive Internet of Medical Things for
Smart Healthcare’, Springer, pp. 49–66.
Boyat, A. K. and Joshi, B. K. , ‘A review paper: noise models in digital image processing’, arXiv
preprint arXiv:1505.03489 .
Buades, A., Coll, B. and Morel, J.-M. , A non-local algorithm for image denoising, in ‘2005 IEEE
Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)’, Vol. 2, IEEE,
Budd, C., Freitag, M. A. and Nichols, N. , ‘Regularization techniques for ill-posed inverse problems
in data assimilation’, Computers & ﬂuids 46(1), 168–173.
Charbonnier, P., Blanc-Féraud, L., Aubert, G. and Barlaud, M. , ‘Deterministic edge-preserving
regularization in computed imaging’, IEEE Transactions on image processing 6(2), 298–311.
Diwakar, M. and Kumar, M. , ‘A review on ct image noise and its denoising’, Biomedical Signal
Processing and Control 42, 73–88.
Duan, J. , Variational and PDE-based methods for image processing, PhD thesis, University of
Dupuis, C. M. , Topics in PDE-based image processing, University of Michigan.
Hu, Y. and Jacob, M. , ‘Higher degree total variation (hdtv) regularization for image recovery’,
IEEE Transactions on Image Processing 21(5), 2559–2571.
Hudagi, M. R., Soma, S. and Biradar, R. L. , Performance analysis of image inpainting using knearest
neighbor, in ‘2022 4th International Conference on Smart Systems and Inventive Technology
(ICSSIT)’, IEEE, pp. 1301–1310.
Jain, P. and Tyagi, V. , ‘A survey of edge-preserving image denoising methods’, Information Systems
Frontiers 18(1), 159–170.
Kadam, C. and Borse, P. , ‘A comparative study of image denoising techniques for medical
images’, image 4(06).
L.elsgoltz , differential equation and calculas of variation, Mir Publishers, USSR, 129820, Moscow
-110, GSP Pervy Rizhsky Pereulok, 2.
Lou, Y., Zeng, T., Osher, S. and Xin, J. , ‘A weighted difference of anisotropic and isotropic total
variation model for image processing’, SIAM Journal on Imaging Sciences 8(3), 1798–1823.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.