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87. Sethian, J. : Level Set Methods and Fast Marching Methods Evolving Interfaces in
Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science.
Cambridge Monograph on Applied and Computational Mathematics, 7th ed. Cambridge University Press (2006)
88. Tadmor, E., Nezzar, S., Vese, L. : A multiscale image representation using hierarchical
pBV, L 2 q decompositions. Multiscale Model. Simul. 2(4), 554–579 (electronic) (2004)
89. Tikhonov, A.N., Arsenin, V.Y. : Solutions of ill-posed problems. V. H. Winston &
Sons, Washington, D.C. : John Wiley & Sons, New York (1977). Translated from the
Russian, Preface by translation editor Fritz John, Scripta Series in Mathematics
90. Tisserand, E., Pautex, J.F., Schweitzer, P. : Analyse et traitement des signaux. Dunod,
Paris (2004)
91. Tr´ emeau, A., Fernandez-Maloigne, C., Bonton, P. : Image num´ erique couleur : De
l’acquisistion au traitement. Dunod (2004)
92. Tsitsiklis, J.N. : Efficient algorithms for globally optimal trajectories. IEEE Transactions on Automatic Control 40(9), 1528–1538 (1995)
93. Vese, L., Chan, T.F. : A multiphase level set framework for image segmentation using
the mumford and shah model. International Journal of Computer Vision 50(3), 271–
293 (2002)
94. Vogel, C.R., Oman, M.E. : Iterative methods for total variation denoising. SIAM J.
Sci. Comput. 17(1), 227–238 (1996). Special issue on iterative methods in numerical
linear algebra (Breckenridge, CO, 1994)
95. WaveLab : URL http://www-stat.stanford.edu/ ~ wavelab/
96. Weiss, P., Aubert, G., Blanc F´ eraud, L. : Efficient schemes for total variation minimization under constraints in image processing. SIAM Journal on Scientific Computing
31(3), 2047–2080 (2009)
97. Yin, W., Goldarb, D., Osher, S. : A comparison of three total variation based texture
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98. Ziemer, W. : Weakly Differentiable Functions - Sobolev Space and Functions of Bounded Variation. Indiana University (1989)
237
86. Serra, J. : Cours de morphologie math´ ematique URL http://cmm.ensmp.fr/ ~ serra/
cours.htm
87. Sethian, J. : Level Set Methods and Fast Marching Methods Evolving Interfaces in
Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science.
Cambridge Monograph on Applied and Computational Mathematics, 7th ed. Cambridge University Press (2006)
88. Tadmor, E., Nezzar, S., Vese, L. : A multiscale image representation using hierarchical
pBV, L 2 q decompositions. Multiscale Model. Simul. 2(4), 554–579 (electronic) (2004)
89. Tikhonov, A.N., Arsenin, V.Y. : Solutions of ill-posed problems. V. H. Winston &
Sons, Washington, D.C. : John Wiley & Sons, New York (1977). Translated from the
Russian, Preface by translation editor Fritz John, Scripta Series in Mathematics
90. Tisserand, E., Pautex, J.F., Schweitzer, P. : Analyse et traitement des signaux. Dunod,
Paris (2004)
91. Tr´ emeau, A., Fernandez-Maloigne, C., Bonton, P. : Image num´ erique couleur : De
l’acquisistion au traitement. Dunod (2004)
92. Tsitsiklis, J.N. : Efficient algorithms for globally optimal trajectories. IEEE Transactions on Automatic Control 40(9), 1528–1538 (1995)
93. Vese, L., Chan, T.F. : A multiphase level set framework for image segmentation using
the mumford and shah model. International Journal of Computer Vision 50(3), 271–
293 (2002)
94. Vogel, C.R., Oman, M.E. : Iterative methods for total variation denoising. SIAM J.
Sci. Comput. 17(1), 227–238 (1996). Special issue on iterative methods in numerical
linear algebra (Breckenridge, CO, 1994)
95. WaveLab : URL http://www-stat.stanford.edu/ ~ wavelab/
96. Weiss, P., Aubert, G., Blanc F´ eraud, L. : Efficient schemes for total variation minimization under constraints in image processing. SIAM Journal on Scientific Computing
31(3), 2047–2080 (2009)
97. Yin, W., Goldarb, D., Osher, S. : A comparison of three total variation based texture
extraction models. J. Vis. Commun. Image Representation 18(3), 240 –252 (2007)
98. Ziemer, W. : Weakly Differentiable Functions - Sobolev Space and Functions of Bounded Variation. Indiana University (1989)