The Beltrami-Mumford-Shah functional

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

Volumn 6667 LNCS, Issue , 2012, Pages 183-193

  Sochen, Nir ^a   Bar, Leah ^a  

^a TEL AVIV UNIVERSITY   (Israel)

Author keywords

Ambrosio Tortorelli functional; anisotropic diffusion; Beltrami framework; Inhomogeneous diffusion; Mumford Shah functional

Indexed keywords

AMBROSIO-TORTORELLI FUNCTIONAL; ANISOTROPIC DIFFUSION; BELTRAMI FLOW; BELTRAMI FRAMEWORK; DIFFUSION TENSOR; EDGE-SETS; MUMFORD-SHAH; MUMFORD-SHAH FUNCTIONALS; PENALTY TERM; SQUARE ROOTS;

COMPUTER VISION; DIFFUSION; OPTICAL ANISOTROPY;

TENSORS;

EID: 84855682340     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-24785-9_16     Document Type: Conference Paper

References (11)

1. Ambrosio, L., Tortorelli, V.M.: Approximation of functionals depending on jumps by elliptic functionals via Γ-convergence. Comm. Pure Appl. Math. 43, 999-1036 (1990)
2. Armijo, L.: Minimization of functions having Lipschitz continuous first partial derivatives. Pacific J. Math. 16(1), 13 (1966)
3. Bar, L., Sochen, N., Kiryati, N.: Image Deblurring in the Presence of Impulsive Noise. International Journal of Computer Vision 70(3), 279-298 (2006)
4. Bar, L., Sochen, N., Kiryati, N.: Semi-Blind Image Restoration via Mumford-Shah Regularization. IEEE Trans. on Image Processing 15(2), 483-493 (2006)
5. Deriche, R., Faugeras, O.: Les EDP en Traitement des Images et Vision par Ordinateur. Traitement du Signal 13(6) (1996)
6. Mumford, D., Shah, J.: Optimal Approximations by Piecewise Smooth Functions and Associated Variational Problems. Comm. on Pure and Applied Math. XLII(5), 577-684 (1989)
7. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 629-639 (1990)
8. Rosenau, P.: Free energy functionals at the high-gradient limit. Phys. Rev. A (Rapid Communications) 41(4), 2227-2230 (1990)
9. Rudin, L., Osher, S., Fatemi, E.: Nonlinear Total Variation based noise removal algorithms. Physica D 60, 259-268 (1992)
10. Sochen, N., Kimmel, R., Malladi, R.: A general framework for low level vision. IEEE Transactions on Image Processing 7, 310-318 (1998)
11. Weickert, J.: Coherence-Enhancing Diffusion Filtering. International Journal of Computer Vision 31(2/3), 111-127 (1999)