#### Filter Results:

- Full text PDF available (80)

#### Publication Year

2002

2018

- This year (8)
- Last 5 years (71)
- Last 10 years (119)

#### Publication Type

#### Co-author

#### Journals and Conferences

Learn More

It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by… (More)

We give a simple technique for verifying the Restricted Isometry Property (as introduced by Candès and Tao) for random matrices that underlies Compressed Sensing. Our approach has two main… (More)

Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we… (More)

- Mark A. Davenport, Petros Boufounos, Michael B. Wakin, Richard G. Baraniuk
- IEEE Journal of Selected Topics in Signal…
- 2010

The recently introduced theory of compressive sensing enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of… (More)

Compressive sensing is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery. In this… (More)

- Mark A. Davenport, Michael B. Wakin
- IEEE Transactions on Information Theory
- 2010

Orthogonal matching pursuit (OMP) is the canonical greedy algorithm for sparse approximation. In this paper we demonstrate that the restricted isometry property (RIP) can be used for a very… (More)

- Richard G. Baraniuk, Michael B. Wakin
- Foundations of Computational Mathematics
- 2009

Many types of data and information can be described by concise models that suggest each data vector (or signal) actually has “few degrees of freedom” relative to its size N . This is the motivation… (More)

- Mark A. Davenport, Marco F. Duarte, +4 authors Richard G. Baraniuk
- Computational Imaging
- 2007

The theory of compressive sensing (CS) enables the reconstruction of a sparse or compressible image or signal from a small set of linear, non-adaptive (even random) projections. However, in many… (More)

- Dharmpal Takhar, Jason N. Laska, +5 authors Richard G. Baraniuk
- Computational Imaging
- 2006

Compressive Sensing is an emerging field based on the revelation that a small numbe r of linear projections of a compressible signal contain enough information for reconstruction and p rocessing. It… (More)

- Michael B. Wakin, Jason N. Laska, +5 authors Richard G. Baraniuk
- 2006

Compressive Sensing is an emerging field based on the revelation that a small group of nonadaptive linear projections of a compressible signal contains enough information for reconstruction and… (More)