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
相关论文
共 50 条
  • [41] Statistical Approach for Nondestructive Incipient Crack Detection and Characterization Using Kullback-Leibler Divergence
    Harmouche, Jinane
    Delpha, Claude
    Diallo, Demba
    Le Bihan, Yann
    IEEE TRANSACTIONS ON RELIABILITY, 2016, 65 (03) : 1360 - 1368
  • [42] A new information-theoretic approach to signal denoising and best basis selection
    Beheshti, S
    Dahleh, MA
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2005, 53 (10) : 3613 - 3624
  • [43] Image denoising using wavelet thresholding and model selection
    Zhong, S
    Cherkassky, V
    2000 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL III, PROCEEDINGS, 2000, : 262 - 265
  • [44] Texture image retrieval based on a Gaussian Mixture Model and similarity measure using a Kullback divergence
    Yuan, H
    Zhang, XP
    2004 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXP (ICME), VOLS 1-3, 2004, : 1867 - 1870
  • [45] Denoising of Electrocardiogram Signal Using S-Transform Based Time–Frequency Filtering Approach
    Ankita Mishra
    Sitanshu Sekhar Sahu
    Rajeev Sharma
    Sudhansu Kumar Mishra
    Arabian Journal for Science and Engineering, 2021, 46 : 9515 - 9525
  • [46] CALIBRATION OF THE UNI-VARIATE COX INGERSOLL ROSS MODEL AND PARAMETERS SELECTION THROUGH THE KULLBACK-LEIBLER DIVERGENCE
    Dang-Nguyen, Stephane
    Le Caillec, Jean-Marc
    Hillion, Alain
    INTERNATIONAL JOURNAL OF THEORETICAL AND APPLIED FINANCE, 2014, 17 (06)
  • [47] Heart Signal Analysis Using Multistage Classification Denoising Model
    Choudhary, Ravi Raj
    Rani, Mamta
    Kaur, Ranjit
    Bhadu, Mahendra
    JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING, 2024, 2024
  • [48] Signal denoising using wavelet and block hidden Markov model
    Liao, ZW
    Lam, ECM
    Tang, YY
    2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS, 2003, : 2468 - 2471
  • [49] Optimal decomposition level selection approach in wavelet threshold denoising algorithm for ECG signal
    Yao Yindi
    Yi Jun
    Zeng Zhibin
    Li Xiong
    Wang Chen
    Li Yuhang
    The Journal of China Universities of Posts and Telecommunications, 2023, 30 (04) : 86 - 104
  • [50] Signal denoising using wavelet packet Hidden Markov model
    Hu, SX
    Liao, ZW
    2004 INTERNATIONAL CONFERENCE ON COMMUNICATION, CIRCUITS, AND SYSTEMS, VOLS 1 AND 2: VOL 1: COMMUNICATION THEORY AND SYSTEMS - VOL 2: SIGNAL PROCESSING, CIRCUITS AND SYSTEMS, 2004, : 751 - 755