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Proceedings Paper

Karhunen-Loeve multispectral and multiscale image restoration
Author(s): Jean-Luc Starck; Philippe Querre; David L. Donoho
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Paper Abstract

We introduce in this paper the notion of WT-KLT and apply it to the problem of noise removal. Decorrelating first the data in the spatial domain using the WT and afterwards using the KLT in spectral domain allows us to derive a roust noise modeling in the WT-KLT space, and hence to filter the transformed data in an efficient way. Experiments are performed in order to derive (i) the best way to calculate the covariance matrix in the case of noisy data, (ii) the best method to correct the noisy WT-KLT coefficients. Finally we investigate if the curvelet transform could be an alternative to the wavelet transform for color image filtering.

Paper Details

Date Published: 4 December 2000
PDF: 12 pages
Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); doi: 10.1117/12.408662
Show Author Affiliations
Jean-Luc Starck, Stanford Univ. and CEA Saclay (France)
Philippe Querre, CEA Saclay (France)
David L. Donoho, Stanford Univ. (United States)

Published in SPIE Proceedings Vol. 4119:
Wavelet Applications in Signal and Image Processing VIII
Akram Aldroubi; Andrew F. Laine; Michael A. Unser, Editor(s)

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