new paper: Relative Optimization for Blind Deconvolution

[Michael Zibulevsky]

Dear Colleagues,

we would like to announce the following paper:

"Relative Optimization for Blind Deconvolution"

by A. Bronstein, M. Bronstein and M. Zibulevsky


Abstract- We propose a relative optimization framework for
quasi-maximum likelihood (ML) blind deconvolution and the
relative Newton method as its particular instance. Special Hessian
structure allows fast approximate Hessian construction and in-
version with complexity comparable to that of gradient methods.
Sequential optimization with gradual change of the smoothing
parameter makes the proposed algorithm very accurate for
sparse or uniformly-like distributed signals. We also propose
the use of rational IIR restoration kernels, which constitute a
richer family of filters than the traditionally used FIR kernels.
Simulation results demonstrate the efficiency of the proposed
methods.


URL of the pdf file:

http://ie.technion.ac.il/~mcib/
or
http://visl.technion.ac.il/bron/alex/publications.html


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Michael Zibulevsky, Ph.D.                  Email: mzib at ee.technion.ac.il
Department of Electrical Engineering       Phone: 972-4-829-4724
Technion - Israel Institute of Technology
Haifa 32000, Israel
http://ie.technion.ac.il/~mcib/            Fax:   972-4-829-4799
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