Kronecker Compressive Sensing

Marco F. Duarte

doi:10.1109/TIP.2011.2165289

2012-02-01

431

PRE

Marco F. Duarte *

Richard G. Baraniuk

DOI：10.1109/TIP.2011.2165289

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摘要

En 中文

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 themeasurement 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.

Keyword:

Compressed sensing

compression algorithms

hyperspectral imaging

multidimensional signal processing

video compression

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期刊

IEEE Transactions on Image Processing

IF:

13.7

论文数:

1.0W

被引数:

8.4W

机构

Rice University

学者数:

1.4W

论文数: 1.2W

被引数: 2.6W

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