Algorithms for signal separation exploiting sparse representations, with application to texture image separation

IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings

Volumn , Issue , 2008, Pages 538-542

  Shoham, Neta ^a   Elad, Michael ^a  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

Denoising; Learned dictionary; Maximum a posteriori probability (MAP); Minimum mean squared error (MMSE); Separation; Sparse representation

Indexed keywords

DENOISING; LEARNED DICTIONARY; MAXIMUM A-POSTERIORI PROBABILITY (MAP); MINIMUM MEAN SQUARED ERROR (MMSE); SPARSE REPRESENTATION;

ALGORITHMS; MAGNETIC ANISOTROPY; MAGNETIC LOGIC DEVICES; MEAN SQUARE ERROR; TEXTURES; THREE TERM CONTROL SYSTEMS;

SEPARATION;

EID: 62749179653     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/EEEI.2008.4736587     Document Type: Conference Paper

References (7)

1. A.M. Bruckstein, D.L. Donoho, and M. Elad, From sparse solutions of systems of equations to sparse modeling of signals and images, to appear in SIAM Review.
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3. S.S. Chen, D.L. Donoho, and M.A. Saunders, Atomic decomposition by basis pursuit, SIAM Review, Volume 43, pp. 129-59, 2001.
4. M. Elad, J-L. Starck, P. Querre, and D.L. Donoho, Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA), Journal on Applied and Computational Harmonic Analysis, Vol. 19, pp. 340-358, 2005.
5. G. Peyre, J. Fadili, and J-L. Starck, Learning adapted dictionaries for geometry and texture separation, Proceedings of SPIE Wavelet XII, Augst 2007.
6. M. Elad and I. Yavneh, A weighted average of sparse representations is better than the sparsest one alone, submitted to IEEE Transactions on Information Theory.
7. M. Elad and M. Aharon, Image denoising via sparse and redundant representation over learned dictionaries, IEEE Trans. on Image Processing, Vol. 15, pp. 3736-3745, December 2006.