Fast and exact solution of Total Variation models on the GPU

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops

Volumn , Issue , 2008, Pages

  Pock, Thomas ^a,b   Unger, Markus ^a   Cremers, Daniel ^b   Bischof, Horst ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b UNIVERSITY OF BONN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

3-D PROBLEMS; DISCRETE OPTIMIZATION TECHNIQUES; EULER-LAGRANGE EQUATIONS; EXACT SOLUTIONS; GPU-BASED ALGORITHMS; GRAPHICS PROCESSING UNIT; PARALLEL ARCHITECTURES; PERFORMANCE EVALUATION; PROBLEM SIZES; TOTAL VARIATION; TV REGULARIZATION; VARIATIONAL MODELING;

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; DIFFERENTIAL EQUATIONS; EQUATIONS OF MOTION; EULER EQUATIONS; FEATURE EXTRACTION; IMAGE PROCESSING; OPTIMIZATION; PARALLEL ALGORITHMS; PATTERN RECOGNITION; THREE DIMENSIONAL;

VARIATIONAL TECHNIQUES;

EID: 51849155111     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2008.4563099     Document Type: Conference Paper

References (35)

1. J.-F. Aujol. Some algorithms for total variation based image restoration. Technical report, CMLA, ENS CACHAN, CNRS, UNIVERSUD, 2008.
2. J.-F. Aujol, G. Gilboa, T. Chan, and S. Osher. Structuretexture image decomposition-modeling, algorithms, and parameter selection. Int. J. Comp. Vis., 67(1):111-136, 2006.
3. A. Bermudez and C. Moreno. Duality methods for solving variational inequalities. Comp. and Math. with Appls., 7:43-58, 1981.
4. A. Bruhn and J. Weickert. Towards ultimate motion estimation: Combining highest accuracy with real-time performance. In Proc. 11th Int. Conf. Comp. Vis., pages 749-755, 2005.
5. J. Carter. Dual Methods for Total Variation-based Image Restoration. PhD thesis, UCLA, Los Angeles, CA, 2001.
6. A. Chambolle. An algorithm for total variation minimizations and applications. J. Math. Imaging Vis., 2004.
7. A. Chambolle. Total variation minimization and a class of binary MRF models. In Energy Minimization Methods in Computer Vision and Pattern Recognition, pages 136-152, 2005.
8. A. Chambolle and P.-L. Lions. Image recovery via total variation minimization and related problems. Numer. Math., 76:167-188, 1997.
9. T. Chan and K. Chen. On a nonlinear multigrid algorithm with primal relaxation for the image total variation minimisation. Numerical Algorithms, 41:387-411, 2006.
10. T. Chan, K. Chen, and J. Carter. Iterative methods for solving the dual formulation arising from image restoration. Electronic Transactions on Numerical Analysis, 26:299-311, 2007.
11. 1 function approximation. SIAM J. Appl. Math., 65(5):1817-1837, 2004.
12. T. Chan, G. Golub, and P. Mulet. A nonlinear primal-dual method for total variation-based image restoration. SIAM J. Sci. Comp., 20(6):1964-1977, 1999.
13. T. Chen, W. Yin, X. Zhou, D. Comaniciu, and T. Huang. Total variation models for variable lighting face recognition. IEEE Trans. Pattern Anal. Mach. Intell., 28(9):1519-1524, 2006.
14. J. Darbon and M. Sigelle. Image restoration with discrete constrained total variation, part i: fast and exact optimization. J. Math. Imaging Vis., 26(3):261-276, 2006.
15. C. Frohn-Schauf, S. Henn, and K. Witsch. Nonlinear multigrid methods for total variation image denoising. Comput. Visual Sci., pages 199-206, 2004.
16. D. Goldfarb and W. Yin. Second-order cone programming methods for total variation-based image restoration. SIAM Journal on Scientific Computing, 27(2):622-645, 2005.
17. D. Goldfarb and W. Yin. Parametric maximum flow algorithms for fast total variation minimization. Technical report, Rice University, 2007.
18. M. Hintermüller and K. Kunisch. Total bounded variation regularization as bilaterally constrained optimization problems. SIAP, 64(4):1311-1333, 2004.
19. P. Huber. Robust Statistics. Wiley, New York, 1981.
20. K. Kolev, M. Klodt, T. Brox, S. Esedoglu, and D. Cremers. Continuous global optimization in multiview 3D reconstruction. In Energy Minimization Methods in Computer Vision and Pattern Recognition, pages 441-452, China, 2007.
21. A. Marquina and S. Osher. Explicit algorithms for a new time dependent model based on level set motion for nonlinear deblurring and noise removal. SIAM J. Sci. Comput., 22:387-405, 2000.
22. M. K. Ng, L. Qi, Y.-F. Yang, and Y.-M. Huang. On semismooth Newton's method for total variation minimization. J. Math. Imaging Vis., 27:265-276, 2007.
23. M. Nikolova. A variational approach to remove outliers and impulse noise. J. Math. Imaging Vis., 20(1-2):99-120, 2004.
24. M. Nikolova, S. Esedoglu, and T. Chan. Algorithms for finding global minimizers of image segmentation and denoising models. SIAM Journal of Applied Mathematics, 66(5):1632-1648, 2006.
25. NVidia. NVidia GeForce 8800 GPU architecture overview. Technical report, NVidia, 2006.
26. NVidia. Nvidia CUDA Compute Unified Device Architecture programming guide 1.1. Technical report, NVidia, 2007.
27. 1-optical-flow image registration. In 10th International Conference on Medical Image Computing and Computer Assisted Intervention, pages 511-518, Brisbane, Australia, 2007.
28. L. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259-268, 1992.
29. J. Savage and K. Chen. An improved and accelerated nonlinear multigrid method for total-variation denoising. J. Math. Imaging Vis., 82(8):1001-1015, 2005.
30. M. Unger, T. Pock, and H. Bischof. Continuous globally optimal image segmentation with local constraints. In Computer Vision Winter Workshop 2008, page accepted, 2008.
31. C. Vogel. A multigrid method for total variation-based image denoising. Progress in Systems and Control Theory, 1995.
32. C. Vogel and M. Oman. Iteration methods for total variation denoising. SIAM J. Sci. Comp., 17:227-238, 1996.
33. P. Weiss, L. Blanc-Féraud, and G. Aubert. Efficient schemes for total variation minimization under constraints in image processing. Technical report, INRIA, 2007.
34. 1 optical flow. In 29th DAGM Symposium on Pattern Recognition, pages 214-223, Heidelberg, Germany, 2007.
35. 1 range image integration. In Proc. 13th Int. Conf. Comp. Vis., Rio de Janeiro, Brazil, 2007.