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Volumn , Issue , 2009, Pages 685-688

Split convex minimization algorithm for signal recovery

Author keywords

Convex optimization methods; Inverse problems; Parallel algorithm; Signal restoration; Variational methods; Wavelet transforms

Indexed keywords

AUDIO SIGNAL PROCESSING; CONVEX OPTIMIZATION; FUNCTIONS; IMAGE RECONSTRUCTION; PARALLEL ALGORITHMS; RECOVERY; RESTORATION; SPEECH COMMUNICATION; WAVELET TRANSFORMS;

EID: 70349466627     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/icassp.2009.4959676     Document Type: Conference Paper
Times cited : (2)

References (16)
  • 1
    • 36749005582 scopus 로고    scopus 로고
    • Two-step iterative shrinkage/thresholding algorithms for image restoration
    • A new TwIST
    • J. M. Bioucas-Dias and M. A. Figueiredo, A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration, IEEE Trans. Image Process., vol. 16, pp. 2992-3004, 2007.
    • (2007) IEEE Trans. Image Process , vol.16 , pp. 2992-3004
    • Bioucas-Dias, J.M.1    Figueiredo, M.A.2
  • 2
    • 0035565698 scopus 로고    scopus 로고
    • Proximity function minimization using multiple Bregman projections, with applications to split feasibility and Kullback-Leibler distance minimization
    • C. L. Byrne and Y. Censor, Proximity function minimization using multiple Bregman projections, with applications to split feasibility and Kullback-Leibler distance minimization, Ann. Oper. Res., vol. 105, pp. 77-98, 2001.
    • (2001) Ann. Oper. Res , vol.105 , pp. 77-98
    • Byrne, C.L.1    Censor, Y.2
  • 3
    • 0031492191 scopus 로고    scopus 로고
    • Image recovery via total variation minimization and related problems
    • A. Chambolle and P.-L. Lions, Image recovery via total variation minimization and related problems, Numer. Math., vol. 76, pp. 167-188, 1997.
    • (1997) Numer. Math , vol.76 , pp. 167-188
    • Chambolle, A.1    Lions, P.-L.2
  • 4
    • 34548678881 scopus 로고    scopus 로고
    • A variational formulation for frame-based inverse problems
    • C. Chaux, P. L. Combettes, J.-C. Pesquet, and V. R. Wajs, A variational formulation for frame-based inverse problems, Inverse Problems, vol. 23, pp. 1495-1518, 2007.
    • (2007) Inverse Problems , vol.23 , pp. 1495-1518
    • Chaux, C.1    Combettes, P.L.2    Pesquet, J.-C.3    Wajs, V.R.4
  • 5
    • 0028543165 scopus 로고
    • Inconsistent signal feasibility problems: Least-squares solutions in a product space
    • P. L. Combettes, Inconsistent signal feasibility problems: Least-squares solutions in a product space, IEEE Trans. Signal Process., vol. 42, pp. 2955-2966, 1994.
    • (1994) IEEE Trans. Signal Process , vol.42 , pp. 2955-2966
    • Combettes, P.L.1
  • 6
    • 0031118954 scopus 로고    scopus 로고
    • Convex set theoretic image recovery by extrapolated iterations of parallel subgradient projections
    • P. L. Combettes, Convex set theoretic image recovery by extrapolated iterations of parallel subgradient projections, IEEE Trans. Image Process., vol. 6, pp. 493-506, 1997.
    • (1997) IEEE Trans. Image Process , vol.6 , pp. 493-506
    • Combettes, P.L.1
  • 7
    • 0038538416 scopus 로고    scopus 로고
    • A block-iterative surrogate constraint splitting method for quadratic signal recovery
    • P. L. Combettes, A block-iterative surrogate constraint splitting method for quadratic signal recovery, IEEE Trans. Signal Process., vol. 51, pp. 1771-1782, 2003.
    • (2003) IEEE Trans. Signal Process , vol.51 , pp. 1771-1782
    • Combettes, P.L.1
  • 8
    • 34548677063 scopus 로고    scopus 로고
    • Proximal thresholding algorithm for minimization over orthonormal bases
    • P. L. Combettes and J.-C. Pesquet, Proximal thresholding algorithm for minimization over orthonormal bases, SIAM J. Optim., vol. 18, pp. 1351-1376, 2007.
    • (2007) SIAM J. Optim , vol.18 , pp. 1351-1376
    • Combettes, P.L.1    Pesquet, J.-C.2
  • 9
    • 39449085530 scopus 로고    scopus 로고
    • P. L. Combettes and J.-C. Pesquet, A Douglas-Rachford splitting approach to nonsmooth convex variational signal recovery, IEEE J. Selected Topics Signal Process., 1, pp. 564-574, 2007.
    • P. L. Combettes and J.-C. Pesquet, A Douglas-Rachford splitting approach to nonsmooth convex variational signal recovery, IEEE J. Selected Topics Signal Process., vol. 1, pp. 564-574, 2007.
  • 10
    • 62649171652 scopus 로고    scopus 로고
    • A proximal decomposition method for solving convex variational inverse problems
    • P. L. Combettes and J.-C. Pesquet, A proximal decomposition method for solving convex variational inverse problems, Inverse Problems, vol. 24, no. 6, 2008.
    • (2008) Inverse Problems , vol.24 , Issue.6
    • Combettes, P.L.1    Pesquet, J.-C.2
  • 11
    • 30844438177 scopus 로고    scopus 로고
    • Signal recovery by proximal forward-backward splitting
    • P. L. Combettes and V. R. Wajs, Signal recovery by proximal forward-backward splitting, Multiscale Model. Simul., vol. 4, pp. 1168-1200, 2005.
    • (2005) Multiscale Model. Simul , vol.4 , pp. 1168-1200
    • Combettes, P.L.1    Wajs, V.R.2
  • 12
    • 7044231546 scopus 로고    scopus 로고
    • An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
    • I. Daubechies, M. Defrise, and C. De Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Comm. Pure Appl. Math., vol. 57, pp. 1413-1457, 2004.
    • (2004) Comm. Pure Appl. Math , vol.57 , pp. 1413-1457
    • Daubechies, I.1    Defrise, M.2    De Mol, C.3
  • 13
    • 78649765600 scopus 로고    scopus 로고
    • Iteratively solving linear inverse problems under general convex constraints
    • I. Daubechies, G. Teschke, and L. Vese, Iteratively solving linear inverse problems under general convex constraints, Inverse Problems and Imaging, vol. 1, pp. 29-46, 2007.
    • (2007) Inverse Problems and Imaging , vol.1 , pp. 29-46
    • Daubechies, I.1    Teschke, G.2    Vese, L.3
  • 14
    • 44049111982 scopus 로고
    • Nonlinear total variation based noise removal algorithms
    • L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D, vol. 60, pp. 259-268, 1992.
    • (1992) Physica D , vol.60 , pp. 259-268
    • Rudin, L.I.1    Osher, S.2    Fatemi, E.3
  • 15
    • 33645712308 scopus 로고    scopus 로고
    • Just relax: Convex programming methods for identifying sparse signals in noise
    • J. A. Tropp, Just relax: Convex programming methods for identifying sparse signals in noise, IEEE Trans. Inform. Theory, vol. 52, pp. 1030-1051, 2006.
    • (2006) IEEE Trans. Inform. Theory , vol.52 , pp. 1030-1051
    • Tropp, J.A.1
  • 16
    • 0020191832 scopus 로고
    • Image restoration by the method of convex projections: Part 1 - theory
    • D. C. Youla and H. Webb, Image restoration by the method of convex projections: Part 1 - theory, IEEE Trans. Medical Imaging, vol. 1, pp. 81-94, 1982.
    • (1982) IEEE Trans. Medical Imaging , vol.1 , pp. 81-94
    • Youla, D.C.1    Webb, H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.