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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, October 28, 2003}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Marc Raimondo}
\centerline{\sl University of Sydney}
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\centerline{\bf Wavelet deconvolution in a periodic setting}
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Abstract:

We study an algorithm which combines Fast Fourier and Fast wavelet 
transforms and can recover a blurred function observed in white noise 
with O(n(log n)2)-steps. In the periodic setting, the method applies 
to most deconvolution problems, including certain "boxcar" kernels,
important 
as a  model of motion  blur but having poor Fourier characteristics. 
The method is tested on simulated lidar data suggested by
underwater remote sensing.  Both visual and numerical results show an
improvement over competing approaches. 

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