Generalized cross validation for multiwavelet shrinkage

Traditional multiwavelet shrinkage denoising techniques require a priori knowledge of noise variance that may not be obtained in some practical situations. By using generalized cross validation (GCV), we propose in this paper a new level-dependent risk estimator for multiwavelet shrinkage that does...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 11; no. 6; pp. 549 - 552
Main Authors Tai-Chiu Hsung, Lun, D.P.-K.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2004
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Discrete transforms
Discrete wavelet transforms
Estimators
Indication
Mean square error methods
Noise
Noise level
Noise reduction
Parameter estimation
Risk
Shrinkage
Signal processing
Signal representations
Simulation
Smoothing methods
Thresholds
White noise
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Summary:Traditional multiwavelet shrinkage denoising techniques require a priori knowledge of noise variance that may not be obtained in some practical situations. By using generalized cross validation (GCV), we propose in this paper a new level-dependent risk estimator for multiwavelet shrinkage that does not require such a priori information. Simulation results verify that the resulted risk estimator gives better indication on threshold selection comparing with the traditional GCV method. Improved denoising performance is then achieved particularly for higher multiplicity multiwavelet shrinkage.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2004.827924