Optimized Projections for Compressed Sensing
Compressed sensing (CS) offers a joint compression and sensing processes, based on the existence of a sparse representation of the treated signal and a set of projected measurements. Work on CS thus far typically assumes that the projections are drawn at random. In this paper, we consider the optimi...
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| Published in: | IEEE transactions on signal processing Vol. 55; no. 12; pp. 5695 - 5702 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York, NY
IEEE
01-12-2007
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| Online Access: | Get full text |
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| Abstract | Compressed sensing (CS) offers a joint compression and sensing processes, based on the existence of a sparse representation of the treated signal and a set of projected measurements. Work on CS thus far typically assumes that the projections are drawn at random. In this paper, we consider the optimization of these projections. Since such a direct optimization is prohibitive, we target an average measure of the mutual coherence of the effective dictionary, and demonstrate that this leads to better CS reconstruction performance. Both the basis pursuit (BP) and the orthogonal matching pursuit (OMP) are shown to benefit from the newly designed projections, with a reduction of the error rate by a factor of 10 and beyond. |
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| AbstractList | Compressed sensing (CS) offers a joint compression and sensing processes, based on the existence of a sparse representation of the treated signal and a set of projected measurements. Work on CS thus far typically assumes that the projections are drawn at random. In this paper, we consider the optimization of these projections. Since such a direct optimization is prohibitive, we target an average measure of the mutual coherence of the effective dictionary, and demonstrate that this leads to better CS reconstruction performance. Both the basis pursuit (BP) and the orthogonal matching pursuit (OMP) are shown to benefit from the newly designed projections, with a reduction of the error rate by a factor of 10 and beyond. |
| Author | Elad, M. |
| Author_xml | – sequence: 1 givenname: M. surname: Elad fullname: Elad, M. organization: Technion-Israel Inst. of Technol., Haifa |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=19886282$$DView record in Pascal Francis |
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| CODEN | ITPRED |
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| SubjectTerms | Applied sciences Basis pursuit (BP) Compressed Compressed sensing compressed sensing (CS) Compressing Computer science Detection Detection, estimation, filtering, equalization, prediction Dictionaries Error analysis Exact sciences and technology Information, signal and communications theory Iterative algorithms Iterative methods Linear systems Matching pursuit algorithms Miscellaneous mutual coherence Optimization optimized projections orthogonal matching pursuit (OMP) Projection Pursuit algorithms Representations Signal and communications theory Signal processing Signal, noise sparse and redundant representations Telecommunications and information theory |
| Title | Optimized Projections for Compressed Sensing |
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