Continuous level anisotropic diffusion for noise removal

Applied Mathematical Modelling

Volumn 36, Issue 8, 2012, Pages 3779-3786

  Zhou, Bin ^a   Mu, Chun Lai ^b   Feng, Jun ^c,d   Wei, Wei ^e  

^a XI'AN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b CHONGQING UNIVERSITY   (China)

^c SICHUAN UNIVERSITY   (China)

^d CHENGDU UNIVERSITY OF TECHNOLOGY   (China)

^e XI'AN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Anisotropic diffusion; Continuous level; Noise removal; Threshold function; Variational model

Indexed keywords

ANISOTROPIC DIFFUSION; CONTINUOUS LEVEL; NOISE REMOVAL; THRESHOLD FUNCTIONS; VARIATIONAL MODELS;

NUMERICAL METHODS; PARTIAL DIFFERENTIAL EQUATIONS;

OPTICAL ANISOTROPY;

EID: 84859842418     PISSN: 0307904X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apm.2011.11.026     Document Type: Article

References (17)

1. El-Fallah A.I., Ford G.E. Mean curvature evolution and surface area scaling in image filtering. IEEE Trans. Image Process. 2002, 6(5):750-753.
2. Perona P., Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Machine Intelligence 1990, 12(7):629-639.
3. Yezzi A. Modified curvature motion for image smoothing and enhancement. IEEE Transactions on Image Processing 2002, 7(3):345-352.
4. L.I. Rudin, S. Osher. Total variation based image restoration with free local constraints. in: IEEE Int. Conf. Image Process., 1994. Proceedings. ICIP-94., pp. 31-35, 1994.
5. Rudin L.I., Osher S., Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 1992, 60(1-4):259-268.
6. Moroney T.J., Turner I.W. A finite volume method based on radial basis functions for two-dimensional nonlinear diffusion equations. Appl. Math. Model. 2006, 30(10):1118-1133.
7. A. Kuijper, p-Laplacian driven image processing. in: IEEE Int. Conf. Image Process., 2007. ICIP 2007., vol. 5, pp. 257-260, 2007.
8. Catté F., Lions P.L., Morel J.M., Coll T. Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal. 1992, 29(1):182-193.
9. Lysaker M., Lundervold A., Tai X.C. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Trans. Image Process. 2004, 12(12):1579-1590.
10. You Y.L., Kaveh M. Fourth-order partial differential equations for noise removal. IEEE Trans. Image Process. 2002, 9(10):1723-1730.
11. Malladi R., Sethian J.A. A unified approach to noise removal, image enhancement, and shape recovery. IEEE Trans. Image Process. 2002, 5(11):1554-1568.
12. Lee S.H., Seo J.K. Noise removal with Gauss curvature-driven diffusion. IEEE Trans. Image Process. 2005, 14(7):904-909.
13. Whitaker R.T., Pizer S.M. A multi-scale approach to nonuniform diffusion. CVGIP Image Understand. 1993, 57. 99-99.
14. Koenderink J.J. The structure of images. Biol. Cybernetics 1984, 50(5):363-370.
15. A.P. Witkin. Scale-space filtering. in: Proc. 8th Int. Joint Conference Artificial Intelligence, vol. 2, pages 1019-1022. Morgan Kaufmann Publishers Inc., 1983.
16. Aubert G., Kornprobst P. Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations 2006, Springer-Verlag, New York Inc.
17. Chambolle A., Lions P.L. Image recovery via Total Variation minimization and related problems. Numerische Mathematik 1997, 76(2):167-188.