Proceedings Paper
Wavelet analysis of random fields and multiresolution Wiener filtering| Format | Member Price | Non-Member Price |
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Paper Abstract
We explore the relationship between random processes and wavelets in multiple dimensions and their application to statistical signal processing. To this end, we introduce a multiresolution Wiener filter (MWF) that is applied to the wavelet coefficients of a random process. The MWF is based upon the multiresolution Wiener-Hopf (MWH) equation, which is derived using orthogonal projection theorem on a Hilbert space. The MWH is applied to the solution of the signal estimation problem for both stationary and fractional Brownian motion (fBm) processes. A theoretical mean square error is calculated for the MWF and its values compared to experimental data.
Paper Details
Date Published: 1 September 1995
PDF: 12 pages
Proc. SPIE 2569, Wavelet Applications in Signal and Image Processing III, (1 September 1995); doi: 10.1117/12.217586
Published in SPIE Proceedings Vol. 2569:
Wavelet Applications in Signal and Image Processing III
Andrew F. Laine; Michael A. Unser, Editor(s)
PDF: 12 pages
Proc. SPIE 2569, Wavelet Applications in Signal and Image Processing III, (1 September 1995); doi: 10.1117/12.217586
Show Author Affiliations
Kevin West Bowman, Georgia Institute of Technology (United States)
Christian Houdre, Georgia Institute of Technology (United States)
Published in SPIE Proceedings Vol. 2569:
Wavelet Applications in Signal and Image Processing III
Andrew F. Laine; Michael A. Unser, Editor(s)
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