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The restricted isometry property and its implications for compressed sensing
Open accessAbstract
It is now well-known that one can reconstruct sparse or compressible signals accurately from a very limited number of measurements, possibly contaminated with noise. This technique known as “compressed sensing” or “compressive sampling” relies on properties of the sensing matrix such as the restricted isometry property . In this Note, we establish new results about the accuracy of the reconstruction from undersampled measurements which improve on earlier estimates, and have the advantage of being more elegant.
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Citations
Cited by
(2015) · Cited by 2,857
Subspace Pursuit for Compressive Sensing Signal Reconstruction
Dai, W., & Milenkovic, O. (2009) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2016006 · Cited by 2,388
Block-Sparse Signals: Uncertainty Relations and Efficient Recovery
Eldar, Y. C., Kuppinger, P., & Bolcskei, H. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2010.2044837 · Cited by 1,313
Model-Based Compressive Sensing
Baraniuk, R. G., Cevher, V., Duarte, M. F., & Hegde, C. (2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2010.2040894 · Cited by 1,308
Signal Processing With Compressive Measurements
Davenport, M. A., Boufounos, P. T., Wakin, M. B., & Baraniuk, R. G. (2010) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2009.2039178 · Cited by 623
Metadata
- Authors
- Candès, E. J.
- Venue
- Comptes Rendus. Mathématique
- Publisher
- MathDoc/Centre Mersenne
- Published
- 2008-04-11
- Volume
- 346
- Issue
- 9-10
- Pages
- 589-592
- ISSN
- 1631-073X (ISSN-L)
- 1778-3569
- OpenAlex
- W2015418199
- Cited by
- 3,686
- References
- 0
- FWCI
- 161.39
- Type
- journal article
- Language
- en
- Open access
- bronze
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