Total variation restoration of speckled images using a split-Bregman algorithm

Proceedings - International Conference on Image Processing, ICIP

Volumn , Issue , 2009, Pages 3717-3720

  Bioucas Dias, José M ^a   Figueiredo, Mário A T ^a  

^a INSTITUTO SUPERIOR TÉCNICO   (Portugal)

Author keywords

Augmented Lagrangian; Bregman iterations; Multiplicative noise; Speckle; Synthetic aperture radar; Total variation

Indexed keywords

ALGEBRA; DIGITAL IMAGE STORAGE; IMAGE PROCESSING; LAGRANGE MULTIPLIERS; OPTOELECTRONIC DEVICES; PHASE NOISE; RADAR; RADAR IMAGING; RESTORATION; SPECKLE; SYNTHETIC APERTURE RADAR; SYNTHETIC APERTURES; UNDERWATER ACOUSTICS;

ART RESTORATION; AUGMENTED LAGRANGIAN METHODS; AUGMENTED LAGRANGIANS; BREGMAN ITERATION; COHERENT IMAGING SYSTEMS; CONSTRAINED PROBLEM; GAUSSIAN ADDITIVE NOISE; GAUSSIANS; LASER IMAGING; MULTIPLICATIVE MODEL; MULTIPLICATIVE NOISE; ORIGINAL IMAGES; RAYLEIGH; SPECKLED IMAGES; TOTAL VARIATION; TOTAL VARIATION REGULARIZATION;

IMAGING SYSTEMS;

EID: 77951946534     PISSN: 15224880     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIP.2009.5414376     Document Type: Conference Paper

References (22)

1. G. Aubert and J. Aujol, "A variational approach to remove multiplicative noise", SIAM Jour. Appl. Math., vol. 68, no. 4, pp. 925-946, 2008.
2. J. Bioucas-Dias, T. Silva, and J. Leitão, "Adaptive restoration of speckled SAR images", IEEE Intern. Geoscience and Remote Sensing Symposium, vol. I, pp. 19-23, 1998.
3. A. Chambolle, "An algorithm for total variation minimization and applications," Jour. Math. Imaging and Vision, vol. 20, pp. 89-97, 2004.
4. T. Chan, S. Esedoglu, F. Park, and A. Yip, "Recent developments in total variation image restoration," in Mathematical Models of Computer Vision, Springer, 2005.
5. T. Chan, G. Golub, and P. Mulet, "A nonlinear primal-dual method for total variation-based image restoration", SIAM Jour. Sci. Comput., vol. 20, pp. 1964-1977, 1999.
6. P. Combettes and V. Wajs, "Signal recovery by proximal forward-backward splitting," SIAM Jour. Multiscale Modeling & Simulation, vol. 4, pp. 1168-1200, 2005.
7. R. Corless, G. Gonnet, D. Hare, D. Jeffrey, and D. Knuth, "On the Lambert W function", Adv. Computational Math. vol. 5, pp. 329-359, 1996.
8. M. Figueiredo, J. Bioucas-Dias, J. Oliveira, and R. Nowak, "On total-variation denoising: A new majorization-minimization algorithm and an experimental comparison with wavalet denoising," IEEE Intern. Conf. Image Processing - ICIP'06, 2006.
9. V. Frost, K. Shanmugan, and J. Holtzman, "A model for radar images and its applications to adaptive digital filtering of multiplicative noise", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 2, pp. 157-166, 1982.
10. T. Goldstein and S. Osher, "The split Bregman method for L1 regularized problems", Tech. Rep. 08-29, Computational and Applied Math., Univ. of California, Los Angeles, 2008.
11. J. Goodman, "Some fundamental properties of speckle", Jour. Opt. Soc. Amer., vol. 66, pp. 1145-1150, Nov. 1976.
12. Y. Huang, M. Ng, and Y. Wen, "A new total variation method for multiplicative noise removal", SIAM Jour. Imaging Sciences, vol. 2, no. 1, pp. 20-40, 2009.
13. J. Nocedal and S. Wright, Numerical Optimization, Springer, 2006.
14. C. Oliver and S. Quegan, Understanding Synthetic Aperture Radar Images, Artech House, 1998.
15. S. Osher, L. Rudin, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D, vol. 60, pp. 259-268, 1992.
16. L. Rudin, P. Lions, and S. Osher, "Multiplicative denoising and deblurring: theory and algorithms", in Geometric Level Set Methods in Imaging, Vision, and Graphics, S. Osher and N. Paragios (editors), pp. 103-120, 2003.
17. J. Shi and S. Osher, "A nonlinear inverse scale space method for a convex multiplicative noise model", Tech. Rep. 07-10, Computational and Applied Math., Univ. of California, Los Angeles, 2007.
18. G. Steidl and T. Teuber, "Removing multiplicative noise by Douglas-Rachford splitting methods", Preprint, University of Mannheim, 2009.
19. X. Tai and C. Wu, "Augmented Lagrangian method, dual methods and split Bregman iteration for ROF model", Tech. Rep. 09-105, Computational and Applied Math., Univ. of California, Los Angeles, 2009.
20. Y. Wang, J. Yang, W. Yin, and Y. Zhang, "A new alternating minimization algorithm for total variation image reconstruction", SIAM Jour. Imaging Sciences, vol. 1, no. 3, pp. 248-272, 2008.
21. W. Yin, S. Osher, D. Goldfarb, J. Darbon, "Bregman iterative algorithms for ℓ1-minimization with applications to compressed sensing", SIAM Jour. Imaging Sciences, vol. 1, no. 1, pp. 143-168, 2008.
22. M. Zhu, S. Wright, and T. Chan, "Duality-based algorithms for total variation image restoration", Tech. Rep. 08-33, Computational and Applied Math., Univ. of California, Los Angeles, 2008.