An adaptive variational model for image decomposition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3757 LNCS, Issue , 2005, Pages 382-397

  Levine, Stacey E ^a  

^a DUQUESNE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR IMAGE DECOMPOSITION; ISOTROPIC SMOOTHING; LEGENDRE-FENCHEL TRANSFORM;

ADAPTIVE SYSTEMS; COLOR IMAGE PROCESSING; COMPUTATIONAL GEOMETRY; IMAGING SYSTEMS; MATHEMATICAL MODELS;

IMAGE ANALYSIS;

EID: 33645695107     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11585978_25     Document Type: Conference Paper

References (23)

1. R. Acar and C. R. Vogel. Analysis of bounded variation penalty methods for ill-posed problems. Inverse Problems, 10(6):1217-1229, 1994.
2. G. Aubert and J.-F. Aujol. Modeling very oscillating signals. Application to image processing. Appl. Math. Optim., 51(2):163-182, 2005.
3. J.-F. Aujol, G. Aubert, L. Blanc-Feraud, and A. Chambolle. Image decomposition application to sar images. In Scale space methods in computer vision: 4th international conference, Scale Space 2003, Isle of Skye, UK, June 10-12, 2003: proceedings/ Lewis D. Griffin, Martin Lillholm (eds.)., Lecture Notes in Computer Science, pages 297-312. Spring: New York, Berlin, 2003.
4. J.-F. Aujol and A. Chambolle. Dual norms and image decomposition models. Int. J. Computer Vision, 63(1):85-104, 2005.
5. P. Blomgren, T. F. Chan, P. Mulet, L. Vese, and W. L. Wan. Variational PDE models and methods for image processing. In Numerical analysis 1999 (Dundee), volume 420 of Chapman & Hall/CRC Res. Notes Math., pages 43-67. Chapman & Hall/CRC, Boca Raton, FL, 2000.
6. A. Chambolle. An algorithm for total variation minimization and applications. J. Math. Imaging Vision, 20(1-2):89-97, 2004.
7. A. Chambolle, R. A. DeVore, N.-y. Lee, and B. J. Lucier. Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage. IEEE Trans. Image Process., 7(3):319-335, 1998.
8. A. Chambolle and P.-L. Lions. Image recovery via total variation minimization and related problems. Numer. Math., 76(2):167-188, 1997.
9. T. F. Chan, S. Esedoglu, and F. Park. A fourth order dual method for staircase reduction in texture extraction and image restoration problems. Technical Report, University of California, Los Angeles, CA, CAM05-28, 2005.
10. T. F. Chan, S. Esedoglu, and F. Park. Image decomposition combining staircase reduction and texture extraction. Technical Report, University of California, Los Angeles, CA, CAM05-18, 2005.
11. Y. Chen, S. Levine, and M. Rao. Variable exponent, linear growth functionals in image processing, submitted to SIAM J. Appl. Math., 2004.
12. D. L. Donoho and I. M. Johnstone. Adapting to unknown smoothness via wavelet shrinkage. J. Amer. Statist. Assoc., 90(432):1200-1224, 1995.
13. I. Ekeland and R. Temam. Convex analysis and variational problems. Translated from the French, Studies in Mathematics and it's Applications, Vol. 1. North-Holland Publishing Co., Amsterdam, 1976.
14. L. C. Evans and R. F. Gariepy. Measure theory and fine properties of functions. Studies in Advanced Mathematics. CRC Press, Boca Raton, FL, 1992.
15. Y. Gousseau and J.-M. Morel. Are natural images of bounded variation? SIAM J. Math. Anal., 33(3):634-648 (electronic), 2001.
16. T. M. Le and L. Vese. Image decomposition using total variation and div(bmo). CAM04-76, UCLA, 2004.
17. F. Malgouyres. Minimizing the total variation under a general convex constraint for image restoration. IEEE Trans. Image Process., 11(12):1450-1456, 2002.
18. Y. Meyer. Oscillating patterns in image processing and nonlinear evolution equations, volume 22 of University Lecture Series. American Mathematical Society, Providence, RI, 2001. The fifteenth Dean Jacqueline B. Lewis memorial lectures.
19. T. Misner, M. Ramsey, and J. Arrowsmith. Multi-frequency, multitemporal brush fire scar analysis in a semi-arid urban environment: Implications for future fire and flood hazards. 2002 meeting of the American Geological Society, 2002.
20. -1 norm. Multiscale Model. Simul., 1(3):349-370 (electronic), 2003.
21. L. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259-268, 1992.
22. L. A. Vese and S. J. Osher. Modeling textures with total variation minimization and oscillating patterns in image processing. J. Sci. Comput., 19(1-3):553-572, 2003. Special issue in honor of the sixtieth birthday of Stanley Osher.
23. L. A. Vese and S. J. Osher. Image denoising and decomposition with total variation minimization and oscillatory functions. J. Math. Imaging Vision, 20(1-2):7-18, 2004. Special issue on mathematics and image analysis.