Model-Based Compressive Sensing
RGRichard G. BaraniukVCVolkan CevherMFMarco F. DuarteCHChinmay Hegde
Rice University · École Polytechnique Fédérale de Lausanne · +1 more institution
Indexed inarxivcrossref
Abstract
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition of sparse or compressible signals that can be well approximated by just K ¿ N elements from an N -dimensional basis. Instead of taking periodic samples, CS measures inner products with M
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Authors
4- RGRichard G. BaraniukCorresponding
Rice University
- VCVolkan Cevher
École Polytechnique Fédérale de Lausanne, Rice University
- MFMarco F. Duarte
Princeton University
- CHChinmay Hegde
Rice University
Topics & keywords
Topics
Keywords
- Compressed sensing
- Restricted isometry property
- Robustness (evolution)
- Compressibility
- SIGNAL (programming language)
- Signal reconstruction
- Signal processing
- Greedy algorithm
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