The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Optimized Projections for Compressed Sensing
Abstract
Compressed sensing (CS) offers a joint compression and sensing processes, based on the existence of a sparse representation of the treated signal and a set of projected measurements. Work on CS thus far typically assumes that the projections are drawn at random. In this paper, we consider the optimization of these projections. Since such a direct optimization is prohibitive, we target an average measure of the mutual coherence of the effective dictionary, and demonstrate that this leads to better CS reconstruction performance. Both the basis pursuit (BP) and the orthogonal matching pursuit (OMP) are shown to benefit from the newly designed projections, with a reduction of the error rate by a factor of 10 and beyond.
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Cites
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Tropp, J. A., & Gilbert, A. C. (2007) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.909108 · Cited by 9,653
Matching pursuits with time-frequency dictionaries
Mallat, S. G., & Zhang, Z. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.258082 · Cited by 9,112
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Candes, E. J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.885507 · Cited by 6,839
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (2001) · SIAM Review · doi:10.1137/s003614450037906x · Cited by 5,085
Pati, Y. C., Rezaiifar, R., & Krishnaprasad, P. S. (2002) · Proceedings of 27th Asilomar Conference on Signals, Systems and Computers · doi:10.1109/acssc.1993.342465 · Cited by 4,323
Greed is Good: Algorithmic Results for Sparse Approximation
Tropp, J. A. (2004) · IEEE Transactions on Information Theory · doi:10.1109/tit.2004.834793 · Cited by 3,635
Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ 1 minimization
Donoho, D. L., & Elad, M. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0437847100 · Cited by 2,898
Self-citation
Cited by
Dictionary Learning-Based Sparse Channel Representation and Estimation for FDD Massive MIMO Systems
Ding, Y., & Rao, B. D. (2018) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2018.2843786 · Cited by 161
Energy-Efficient Multi-Mode Compressed Sensing System for Implantable Neural Recordings
Suo, Y., Zhang, J., Xiong, T., Chin, P. S., Etienne-Cummings, R., & Tran, T. D. (2014) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2014.2359180 · Cited by 47
A closed-loop compressive-sensing-based neural recording system
Zhang, J., Mitra, S., Suo, Y., Cheng, A., Xiong, T., Michon, F., Welkenhuysen, M., Kloosterman, F., Chin, P. S., Hsiao, S., et al. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/3/036005 · Cited by 33
Metadata
- Authors
- Elad, M.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2007-12
- Volume
- 55
- Issue
- 12
- Pages
- 5695-5702
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2134033146
- Cited by
- 835
- References
- 17
- FWCI
- 24.60
- Type
- journal article
- Language
- en
- Open access
- closed
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