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Proceedings Paper

Construction of signal-dependent Cohen's-class time-frequency distributions using iterative blind deconvolution
Author(s): Andrew E Yagle; Jose E. Torres-Fernandez
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Paper Abstract

The problem of kernel design for Cohen time-frequency distributions is formulated as a blind deconvolution problem. It is shown that the iterative blind deconvolution method (IBDM) used in image restoration problems can be successfully applied to solve the kernel design problem. We obtain the following results: (1) the rate of convergence depends on which domains the constraints are imposed (2) certain constraints are needed for algorithm convergence (3) the more constrained the kernel design is, the faster the rate of convergence (4) there are tradeoffs between constraints, e.g., compact support vs. satisfaction of marginals; (5) time-frequency distributions which are more amenable to visual interpretation can be obtained using this algorithm.

Paper Details

Date Published: 24 December 2003
PDF: 12 pages
Proc. SPIE 5205, Advanced Signal Processing Algorithms, Architectures, and Implementations XIII, (24 December 2003); doi: 10.1117/12.504467
Show Author Affiliations
Andrew E Yagle, Univ. of Michigan (United States)
Jose E. Torres-Fernandez, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 5205:
Advanced Signal Processing Algorithms, Architectures, and Implementations XIII
Franklin T. Luk, Editor(s)

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