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Proceedings Paper

Geometrical priors in a Bayesian approach to improve wavelet threshold procedures
Author(s): Maarten Jansen; Adhemar Bultheel
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Paper Abstract

Wavelet threshold algorithms replace wavelet coefficients with small magnitude by zero and keep or shrink the other coefficients. This is basically a local procedure, since wavelet coefficients characterize the local regularity of a function. Although a wavelet transform has decorrelating properties, structures in images, like edges, are never decorrelated completely, and these structures appear in the wavelet coefficients. We therefore introduce geometrical prior model for configurations of large wavelet coefficients and combine this with the local characterization of a classical threshold procedure into a Bayesian framework. The threshold procedure selects the large coefficients in the actual image. This observed configuration enters the prior model, which, by itself, only describes configurations, not coefficient values. In this way, we can compute for each coefficient the probability of being `sufficiently clean'.

Paper Details

Date Published: 26 October 1999
PDF: 11 pages
Proc. SPIE 3813, Wavelet Applications in Signal and Image Processing VII, (26 October 1999); doi: 10.1117/12.366813
Show Author Affiliations
Maarten Jansen, Katholieke Univ. Leuven (Belgium)
Adhemar Bultheel, Katholieke Univ. Leuven (Belgium)


Published in SPIE Proceedings Vol. 3813:
Wavelet Applications in Signal and Image Processing VII
Michael A. Unser; Akram Aldroubi; Andrew F. Laine, Editor(s)

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