Adaptive Fourth Order Partial Differential Equation Filter from the Webers Total Variation for Image Restoration

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By substituting an anisotropic diffusion operator for the isotropic Laplace operator in the directed diffusion equation, an improvd directed diffusion equation model is proposed. To overcome the staircasing effects and simultaneously avoid edge blurring, this paper proposed an adaptive fourth order partial differential equation from the Webers Total Variation for Image Restoration. This functional is not only to use Laplace operator but also to add the human psychology system, this paper show numerical evidence of the power of resolution of the model with respect to other known models as the Perona-Malik model. Compared results disctincly demonstrate the superiority of our proposed scheme , in terms of removing noise while sharply maintaining the edge features.

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394-400

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December 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] T.F. Chan, S. Osher, J. Shen, The digital TV filter and nonlinear denoising , IEEE Trans. Image Process. 10 (2) (2001) 231–241.

DOI: 10.1109/83.902288

Google Scholar

[2] A. Chambolle, P.L. Lions, Image recovery via total variational minimization and related problems, Numer. Math. 76 (1997) 167–188.

DOI: 10.1007/s002110050258

Google Scholar

[3] T.F. Chan, J. Shen , Variational restoration of non-flat image features: models and algorithms, SIAM J. Appl. Math. 61 (4) (2000) 1338–1361.

DOI: 10.1137/s003613999935799x

Google Scholar

[4] T.F. Chan, J. Shen, Mathematical models for local nontexture inpaintings, SIAM J. Appl. Math. 62 (3) (2001) 1019–1043.

DOI: 10.1137/s0036139900368844

Google Scholar

[5] D,M. Strong, T.F. Chan, Spatially and Scale Adaptive Total Variation Based Regularization and Anisotropic Diffusion in image Processing, UCLACAM report 96-46, November, (1996).

Google Scholar

[6] D.M. Strong, Adaptive Total Varation Minimizing Image Restoration, Ph.D. Thesis, UCLACAM report 97-38, August, (1997).

Google Scholar

[7] Scherzer,O. 1998. Denoising with higher order derivatives of bounded variation and an application to parameter estimation. Computing, 60: 1-27.

DOI: 10.1007/bf02684327

Google Scholar

[8] L. Rudin, S. Osher, Total variation based image restoration with free local constraints, in: Proceedings of the First IEEE ICIP Conference, vol. 1, 1994, p.31–35.

DOI: 10.1109/icip.1994.413269

Google Scholar

[9] L. Rudin, S. Osher, E. Fatemi, Nonlinear total variation based noise removal algorithms, Physical D 60 (1992) 259–268.

DOI: 10.1016/0167-2789(92)90242-f

Google Scholar

[10] C.R. Vogel, M.E. Oman, Iterative methods for total variation denoising, SIAM J. Sci. Comput. 17 (1) (1996) 227–238.

DOI: 10.1137/0917016

Google Scholar

[11] L. Rudin, S. Osher, E. Fatemi, Nonlinear total variation based noise removal algorithms, Physical D 60 (1992) 259–268.

DOI: 10.1016/0167-2789(92)90242-f

Google Scholar

[12] M, Lysaker, X, C, Tai, Iterative image restoration combining total variation minimization and a second-orderfunction. Int.J. Comput. Vision 66(2006)5-18.

DOI: 10.1007/s11263-005-3219-7

Google Scholar

[13] F.L.C. Shen.J. Fan.C. Shen, Image restoration combining total varational filter and a fourth order filter,J. Vis. Commun. Image R. 18(2007)322-330.

DOI: 10.1016/j.jvcir.2007.04.005

Google Scholar