Compressed sampling strategies for tomography

Yan Kaganovsky; Daheng Li; Andrew Holmgren; HyungJu Jeon; Kenneth P. MacCabe; David G. Politte; Joseph O'Sullivan; Lawrence Carin; David J. Brady

doi:10.1364/JOSAA.31.001369

Compressed sampling strategies for tomography

Yan Kaganovsky
, Daheng Li
, Andrew Holmgren
, HyungJu Jeon
, Kenneth P. MacCabe
, David G. Politte
, Joseph O'Sullivan
, Lawrence Carin
, David J. Brady

University of Missouri-St. Louis

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

Original language	American English
Journal	Journal of the Optical Society of America A
Volume	31
DOIs	https://doi.org/10.1364/JOSAA.31.001369
State	Published - Jun 9 2014

Disciplines

Engineering

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@article{d7ef22525e44439d9db407a0a62fe8eb,

title = "Compressed sampling strategies for tomography",

author = "Yan Kaganovsky and Daheng Li and Andrew Holmgren and HyungJu Jeon and MacCabe, \{Kenneth P.\} and Politte, \{David G.\} and Joseph O'Sullivan and Lawrence Carin and Brady, \{David J.\}",

note = "We investigate new sampling strategies for projection tomography, enabling one to employ fewer measurements than expected from classical sampling theory without significant loss of information. Inspired by compressed sensing, our approach is based on the understanding that many real objects are compressible in some known representation, implying that the number of degrees of freedom defining an object is often much smaller than the number of pixels/voxels.",

year = "2014",

month = jun,

day = "9",

doi = "10.1364/JOSAA.31.001369",

language = "American English",

volume = "31",

journal = "Journal of the Optical Society of America A",

}

TY - JOUR

T1 - Compressed sampling strategies for tomography

AU - Kaganovsky, Yan

AU - Li, Daheng

AU - Holmgren, Andrew

AU - Jeon, HyungJu

AU - MacCabe, Kenneth P.

AU - Politte, David G.

AU - O'Sullivan, Joseph

AU - Carin, Lawrence

AU - Brady, David J.

N1 - We investigate new sampling strategies for projection tomography, enabling one to employ fewer measurements than expected from classical sampling theory without significant loss of information. Inspired by compressed sensing, our approach is based on the understanding that many real objects are compressible in some known representation, implying that the number of degrees of freedom defining an object is often much smaller than the number of pixels/voxels.

PY - 2014/6/9

Y1 - 2014/6/9

UR - https://www.osapublishing.org/josaa/abstract.cfm?uri=josaa-31-7-1369

U2 - 10.1364/JOSAA.31.001369

DO - 10.1364/JOSAA.31.001369

M3 - Article

VL - 31

JO - Journal of the Optical Society of America A

JF - Journal of the Optical Society of America A

ER -

Compressed sampling strategies for tomography

Disciplines

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