Presentation 2009-07-01
Compressed Sensing : Basic Principle and State-of-the-Art Results
Akira HIRABAYASHI,
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Abstract(in English) Compressed sensing stands for a technique which reduces a number of observation data of a target signal as few as possible and reconstructs the signal from those few data under an assumption that the target signal is sparse or compressible. Even though both of the observation process and the reconstruction method are subject to be designed, the latter is frequently fixed in many studies. The most popular reconstruction method is the l_1-norm minimization, and several conditions under which the minimization principle works correctly have been provided. Compressed sensing has been attracting much attention for these several years. This is not only because compressed sensing is theoretically interesting, but also a wide range of application areas expands behind compressed sensing. This tutorial gives explanations of the basic idea of the compressed sensing and several state-of-the-art results in the field with concrete examples.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Sparse signals / compressibility / l_1-norm minimization / null space property / restricted isometry property
Paper # CAS2009-11,VLD2009-16,SIP2009-28
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Committee SIP
Conference Date 2009/6/24(1days)
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Registration To Signal Processing (SIP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Compressed Sensing : Basic Principle and State-of-the-Art Results
Sub Title (in English)
Keyword(1) Sparse signals
Keyword(2) compressibility
Keyword(3) l_1-norm minimization
Keyword(4) null space property
Keyword(5) restricted isometry property
1st Author's Name Akira HIRABAYASHI
1st Author's Affiliation Graduate School of Medicine, Yamaguchi University()
Date 2009-07-01
Paper # CAS2009-11,VLD2009-16,SIP2009-28
Volume (vol) vol.109
Number (no) 112
Page pp.pp.-
#Pages 6
Date of Issue