A projected gradient algorithm for image restoration under Hessian matrix-norm regularization

Proceedings - International Conference on Image Processing, ICIP

Volumn , Issue , 2012, Pages 3029-3032

  Lefkimmiatis, Stamatios ^a   Unser, Michael ^a  

^a EPFL   (Switzerland)

Author keywords

Hessian matrix norms; image restoration; Linear inverse problems; mixed norm regularization

Indexed keywords

ADDITIVE GAUSSIAN NOISE; CONVEX CONSTRAINTS; FUNCTIONALS; GRADIENT BASED ALGORITHM; HESSIAN MATRICES; HIGHER-ORDER; IMAGE DEBLURRING; LINEAR INVERSE PROBLEMS; MIXED-NORM; PROJECTED GRADIENT; REGULARIZATION FRAMEWORK; STAIRCASE EFFECT; TOTAL VARIATION;

GAUSSIAN NOISE (ELECTRONIC); IMAGE ENHANCEMENT; INVERSE PROBLEMS; MATRIX ALGEBRA;

IMAGE RECONSTRUCTION;

EID: 84875846282     PISSN: 15224880     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIP.2012.6467538     Document Type: Conference Paper

References (13)

1. P. C. Hansen, J. G. Nagy, and D. P. O'Leary, Deblurring Images: Matrices, Spectra, and Filtering, SIAM,2006.
2. L. Rudin, S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D, vol. 60, pp. 259-268, 1992.
3. M. Lysaker and X.-C. Tai, "Iterative image restoration combining total variation minimization and a second-order functional," Int. J. Computer Vision, vol. 66, pp. 5-18, 2006.
4. G. Steidl, "A note on the dual treatment of higher-order regularization functionals," Computing, vol. 76, pp. 135-148,2006.
5. K. Bredies, K. Kunisch, and T. Pock, "Total generalized variation," SIAM J. Imaging Sci., vol. 3, no. 3, pp. 492-526, 2010.
6. S. Lefkimmiatis, A. Bourquard, and M. Unser, "Hessian-based norm regularization for image restoration with biomedical applications," IEEE Trans. Image Processing, vol. 21, no. 3, pp. 983-995,2012.
7. I. Daubechies, M. Defrise, and C. De Mol, "An iterative thresholding algorithm for linear inverse problems with a sparsity constraint," Commun. Pure Appl. Math., vol. 57, pp. 1413-1457,2004.
8. M.A.T. Figueiredo, J.M. Bioucas-Dias, and R.D. Nowak, "Majorization-minimization algorithms for wavelet-based image restoration," IEEE Trans. Image Processing, vol. 16, pp. 2980-2991,2007.
9. A. Beck and M. Teboulle, "A fast iterative shrinkagethresholding algorithm for linear inverse problems," SIAM J. Imaging Sciences, vol. 2, pp. 183-202,2009.
10. A. Beck and M. Teboulle, "Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems," IEEE Trans. Image Processing, vol. 18, pp. 2419-2434,2009.
11. 1,q-norm balls for grouped feature selection," in Proceedings of the European conference on Machine learning and knowledge discovery in databases (ECMLPKDD'11), 2011, vol. 3, pp. 305-317.
12. R. T. Rokcafellar, Convex Analysis, Princeton, NJ: Princeton Univ. Press, 1970.
13. 2)," Soviet Math. Dokl, vol. 27, pp. 372-376, 1983.