The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Kronecker Compressive Sensing
Abstract
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.
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Citations
Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Cited by
Sparsity-Aware Learning and Compressed Sensing: An Overview
Theodoridis, S., Kopsinis, Y., & Slavakis, K. (2014) · Academic Press Library in Signal Processing · doi:10.1016/b978-0-12-396502-8.00023-1 · Cited by 14
Metadata
- Authors
- Duarte, M. F., & Baraniuk, R. G.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2012-02
- Volume
- 21
- Issue
- 2
- Pages
- 494-504
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2001380973
- Cited by
- 546
- References
- 1
- FWCI
- 36.13
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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