Construction of signal-dependent Cohen's-class time-frequency distributions using iterative blind deconvolution

被引：1
|
作者：
Yagle, AE [1 ]
Torres-Fernández, JE [1 ]
机构：
[1] Univ Michigan, Dept EECS, Ann Arbor, MI 48109 USA
来源：
ADVANCED SIGNAL PROCESSING ALGORITHMS, ARCHITECTURES, AND IMPLEMENTATIONS XIII | 2003年 / 5205卷
关键词：
time-frequency distributions; blind deconvolution;
D O I：
10.1117/12.504467
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：47 / 58
页数：12
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