Image decomposition, image restoration, and texture modeling using total variation minimization and the H -1 norm

IEEE International Conference on Image Processing

Volumn 1, Issue , 2003, Pages 689-692

  Osher, Stanley ^a   Solé, Andrés ^b   Vese, Luminita ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITAT POMPEU FABRA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

DENOISING; ENERGY MINIMIZATION; IMAGE DECOMPOSITION; IMAGE FUNCTIONS;

APPROXIMATION THEORY; COMPUTER VISION; FRACTALS; FUNCTIONS; MATHEMATICAL MODELS; NONLINEAR EQUATIONS; OSCILLATIONS; PARTIAL DIFFERENTIAL EQUATIONS; PROBLEM SOLVING;

IMAGE RECONSTRUCTION;

EID: 0344704295     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. S. Casadei, S. Mitter, P. Perona, "Boundary detection in piecewise homogeneous textured images", LNCS, Vol. 588, 174-183, 1992.
2. F. Malgouyres, "Mathematical analysis of a model which combines total variation and wavelet for image restoration", Journal of information processes, 2(1): 1-10, 2002.
3. F. Malgouyres, "Combining total variation and wavelet packet approaches for image deblurring", Proceedings, IEEE Workshop on Variational and Level Set Methods in Computer Vision, 57-64, 2001.
4. F. Malgouyres, "Minimizing the total variation under a general convex constraint for image restoration", to appear in IEEE T IP.
5. Y. Meyer, "Oscillating Patterns in Image Processing and Nonlinear Evolution Equations", University Lecture Series, 22, AMS 2002.
6. D. Mumford, B. Gidas, "Stochastic models for generic images", Quarterly of Applied Mathematics, 59(1): 85-111, 2001.
7. L. Rudin, S. Osher, "Total Variation based image restoration with free local constraints", Proceedings of the IEEE ICIP 1994, I, 31-35.
8. L. Rudin, S. Osher, E. Fatemi, "Nonlinear total variation based noise removal algorithms", Physica D, 60, 259-268, 1992.
9. L. Vese, S. Osher, "Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing", UCLA CAM Report 02-19, May 2002, to appear, JSC.
10. S.C. Zhu, Y.N. Wu, D. Mumford, "Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling", IJCV 27(2): 107-126, 1998.
11. S.C. Zhu, Y.N. Wu, D. Mumford, "Minimax entropy principle and its application to texture modeling", Neural Computation, 9(8): 1627-1660, 1997.