Compressed sensing of color images

Signal Processing

Volumn 90, Issue 12, 2010, Pages 3122-3127

  Majumdar, Angshul ^a   Ward, Rabab K ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Color imaging; Compressed sensing; Sparse reconstruction

Indexed keywords

COLOR CHANNELS; COLOR IMAGES; COLOR IMAGING; COMPRESSED SENSING; COMPRESSIVE SAMPLING; HIGHLY-CORRELATED; OPTIMIZATION ALGORITHMS; OPTIMIZATION METHOD; RANDOM PROJECTIONS; RECONSTRUCTION METHOD; RECONSTRUCTION PROBLEMS; SPARSE ALGORITHMS; SPARSE RECONSTRUCTION;

CONCRETE BEAMS AND GIRDERS; OPTIMIZATION; REPAIR; SIGNAL RECONSTRUCTION; WAVELET TRANSFORMS;

COLOR;

EID: 77955414367     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2010.05.016     Document Type: Article

References (17)

1. M. Wakin, J. Laska, M. Duarte, D. Baron, S. Sarvotham, D. Takhar, K. Kelly, and R. Baraniuk An architecture for compressive imaging IEEE Int. Conf. Image Process. 2006 1273 1276
2. D. Donoho For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution Comm. Pure Appl. Math. 59 6 2006 797 829 (Pubitemid 43667226)
3. R. Chartrand, and W. Yin Iteratively reweighted algorithms for compressive sensing IEEE Int. Conf. Acoust., Speech Signal Process. 2008 3869 3872
4. R. Saab, Yilmaz, Sparse recovery by non-convex optimizationinstance optimality, arXiv:0809.0745v1 [cs.IT].
5. D.L. Donoho Compressed sensing IEEE Trans. Inf. Theory 52 4 2006 1289 1306
6. E.J. Candes, and T. Tao Near optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inf. Theory 52 12 2006 5406 5425
7. P. Nagesh, and B. Li Compressive imaging of color images IEEE Int. Conf. Acoust., Speech Signal Process. 2009
8. Y.C. Eldar, M. Mishali, Robust recovery of signals from a union of subspaces, IEEE Trans. Inf. Theory 53(11) (2009) 53025316
9. J. Huang, T. Zhang, The benefit of group sparsity, arXiv:0901.2962v1.
10. M. Stojnic, F. Parvaresh, B. Hassibi, On the reconstruction of block-sparse signals with an optimal number of measurements, arXiv:0804.0041v1.
11. Y. Kim, J. Kim, and Y. Kim Blockwise sparse regression Stat. Sin. 16 2006 375 390
12. L. Meier, S. van de Geer, and P. Buhlmann The group lasso for logistic regression J. R. Stat. Soc. B 70 2008 53 71
13. B. Turlach, W. Venables, and S. Wright Simultaneous variable selection Technometrics 47 2005 349 363
14. M. Yuan, and Y. Lin Model selection and estimation in regression with grouped variables J. R. Stat. Soc. B 68 2006 49 67
15. I. Daubechies, R. DeVore, M. Fornasier, C. Sinan Gntrk, Iteratively re-weighted least squares minimization for sparse recovery, preprint, 2008.
16. H. Mohimani, M. Babaie-Zadeh, and C. Jutten A fast approach for overcomplete sparse decomposition based on smoothed ℓ (0) norm IEEE Trans. Signal Process. 57 1 2008 289 301
17. http://www.mathworks.com/matlabcentral/fileexchange/23422