WSEAS Transactions on Computer Research
Print ISSN: 1991-8755, E-ISSN: 2415-1521
Volume 9, 2021
Case Studies on Discrete Wavelet Denoising via Kernel based Nonlinear Component Analysis
Authors: , ,
Abstract: Complex real world problems are essentially nonlinear. Linear models are relatively simple but inaccurate todescribe the nonlinear aspects of dynamic system behaviors. Denoising techniques have been broadly applied to numerousapplications in the spatial domain, frequency domain, and time domain. To increase the adaptability of denoisingtechniques to signal processing of arbitrary nonlinear systems, kernel based nonlinear component analysis is proposed toenhance wavelet denoising. In the multilevel wavelet decomposition, the low frequency approximations and high frequencydetails are produced at each level. Discrete wavelet transform (DWT) will help to decompose low frequencyapproximations exclusively at all the succeeding levels, while wavelet packet transform decomposes both approximationsand high frequency details at each level. DWT is selected for wavelet denoising in this study, where details at each leveland the approximation at specified level are all subject to simplification using nonlinear component analysis. Case studiesof typical nonlinear denoising problems in various domains are conducted. The results manifest strong feasibility andadaptability across diverse denoising problems of nonlinear systems.
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Keywords: Nonlinear Systems, Denoising, Discrete Wavelet Transform (DWT), Nonlinear Component Analysis (NCA)
Pages: 8-12
DOI: 10.37394/232018.2021.9.2