A model selection approach to signal denoising using Kullback's symmetric divergence

被引:5
|
作者
Bekara, Maiza
Knockaert, Luc
Seghouane, Abd-Krim
Fleury, Gilles
机构
[1] Ecol Super Elect Serv Mesures, F-91192 Gif Sur Yvette, France
[2] IMEC INTEC UGENT, B-900 Ghent, Belgium
关键词
signal denoising; model selection; information criterion;
D O I
10.1016/j.sigpro.2005.03.023
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
We consider the determination of a soft/hard coefficients threshold for signal recovery embedded in additive Gaussian noise. This is closely related to the problem of variable selection in linear regression. Viewing the denoising problem as a model selection one, we propose a new information theoretical model selection approach to signal denoising. We first construct a statistical model for the unknown signal and then try to find the best approximating model (corresponding to the denoised signal) from a set of candidates. We adopt the Kullback's symmetric divergence as a measure of similarity between the unknown model and the candidate model. The best approximating model is the one that minimizes an unbiased estimator of this divergence. The advantage of a denoising method based on model selection over classical thresholding approaches, resides in the fact that the threshold is determined automatically without the need to estimate the noise variance. The proposed denoising method, called KICc-denoising (Kullback Information Criterion corrected) is compared with cross validation (CV), minimum description length (MDL) and the classical methods SureShrink and VisuShrink via a simulation study based on three different type of signals: chirp, seismic and piecewise polynomial. (C) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码:1400 / 1409
页数:10
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