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Proceedings Paper

Phase-space analysis of sparse signals and compressive sensing
Author(s): Markus E. Testorf
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Paper Abstract

Compressive sampling schemes for sparse signals are investigated in the framework of phase-space optics. Phasespace representations are used to identify signal sparsity and construct compressive sensing schemes. Both linear and nonlinear compressive sampling methods are interpreted as applications of Lukosz superresolution. For two iterative methods, the l1-magic algorithm and the CLEAN algorithm, numerical experiments are performed to determine the practical limits of sparse signal recovery. In addition, the phase-space interpretation is used to construct a phase retrieval algorithm for signals with a sparse phase space.

Paper Details

Date Published: 15 October 2012
PDF: 11 pages
Proc. SPIE 8500, Image Reconstruction from Incomplete Data VII, 850004 (15 October 2012); doi: 10.1117/12.929758
Show Author Affiliations
Markus E. Testorf, Dartmouth College (United States)


Published in SPIE Proceedings Vol. 8500:
Image Reconstruction from Incomplete Data VII
Philip J. Bones; Michael A. Fiddy; Rick P. Millane, Editor(s)

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