Compressive sensing

Richard G. Baraniuk

doi:10.1109/MSP.2007.4286571

Compressive sensing

Richard G. Baraniuk

Research output: Contribution to journal › Article › peer-review

3765 Scopus citations

Abstract

Signal acquisition based on compressive sensing can be more efficient than traditional sampling for sparse or compressible signals. This method captures and represents signals at a rate significantly below the Nyquist rate and employs nonadaptive linear projections preserving signal structure. Then, the signal is reconstructed using an optimization technique. The least squares optimization used is inadequate for signal reconstruction, and other types of convex optimization must be invoked.

Original language	English (US)
Pages (from-to)	118-120+124
Journal	IEEE Signal Processing Magazine
Volume	24
Issue number	4
DOIs	https://doi.org/10.1109/MSP.2007.4286571
State	Published - Jul 2007

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/MSP.2007.4286571

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title = "Compressive sensing",

abstract = "Signal acquisition based on compressive sensing can be more efficient than traditional sampling for sparse or compressible signals. This method captures and represents signals at a rate significantly below the Nyquist rate and employs nonadaptive linear projections preserving signal structure. Then, the signal is reconstructed using an optimization technique. The least squares optimization used is inadequate for signal reconstruction, and other types of convex optimization must be invoked.",

author = "Baraniuk, {Richard G.}",

note = "Funding Information: This work was supported by grants from NSF, DARPA, ONR, AFOSR, and the Texas Instruments (TI) Leadership University Program. Special thanks are due to TI for the DMD array used in the single-pixel camera. Thanks also to the Rice DSP group and Ron DeVore for many enlightening discussions and Justin Romberg for help with the reconstruction in Figure 3. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.",

year = "2007",

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journal = "IEEE Signal Processing Magazine",

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N1 - Funding Information: This work was supported by grants from NSF, DARPA, ONR, AFOSR, and the Texas Instruments (TI) Leadership University Program. Special thanks are due to TI for the DMD array used in the single-pixel camera. Thanks also to the Rice DSP group and Ron DeVore for many enlightening discussions and Justin Romberg for help with the reconstruction in Figure 3. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2007/7

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N2 - Signal acquisition based on compressive sensing can be more efficient than traditional sampling for sparse or compressible signals. This method captures and represents signals at a rate significantly below the Nyquist rate and employs nonadaptive linear projections preserving signal structure. Then, the signal is reconstructed using an optimization technique. The least squares optimization used is inadequate for signal reconstruction, and other types of convex optimization must be invoked.

AB - Signal acquisition based on compressive sensing can be more efficient than traditional sampling for sparse or compressible signals. This method captures and represents signals at a rate significantly below the Nyquist rate and employs nonadaptive linear projections preserving signal structure. Then, the signal is reconstructed using an optimization technique. The least squares optimization used is inadequate for signal reconstruction, and other types of convex optimization must be invoked.

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DO - 10.1109/MSP.2007.4286571

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Compressive sensing

Abstract

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