Kronecker Compressive Sensing

Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals; the...

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Vydané v:	IEEE transactions on image processing Ročník 21; číslo 2; s. 494 - 504
Hlavní autori:	Duarte, M. F., Baraniuk, R. G.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.02.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Applied sciences Approximation Atmospheric measurements Compressed sensing compression algorithms Derivation Detection Detection, estimation, filtering, equalization, prediction Exact sciences and technology Hyperspectral imaging Image coding Image processing Information, signal and communications theory Mathematical analysis Matrices Matrix methods multidimensional signal processing Multiplexing Particle measurements Representations Sampling, quantization Signal and communications theory Signal processing Signal, noise Telecommunications and information theory video compression Video coding Hyperspectral imaging sensor Multidimensional signal processing Dimensionality Signal restoration video compression Hyperspectral imagery compression algorithms Sparse representation hyperspectral imaging Video signal processing Compressed sensing
ISSN:	1057-7149, 1941-0042, 1941-0042
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Popis
Shrnutí:	Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals; the construction of sparsifying bases and measurement systems for such signals is complicated by their higher dimensionality. In this paper, we propose the use of Kronecker product matrices in CS for two purposes. First, such matrices can act as sparsifying bases that jointly model the structure present in all of the signal dimensions. Second, such matrices can represent the measurement protocols used in distributed settings. Our formulation enables the derivation of analytical bounds for the sparse approximation of multidimensional signals and CS recovery performance, as well as a means of evaluating novel distributed measurement schemes.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	1057-7149 1941-0042 1941-0042
DOI:	10.1109/TIP.2011.2165289