An Adaptive Denoising Algorithm for Noisy Chaotic Signals Based on Local Sparse Representation
An Adaptive Denoising Algorithm for Noisy Chaotic Signals Based on Local Sparse Representation
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DOI：
10.1088/0256-307x/26/3/030501
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发表时间：
2009-03
影响因子：
3.5
通讯作者：
Zong-Bo Xie;Jiu-chao Feng
中科院分区：
文献类型：
--
作者：
Zong-Bo Xie;Jiu-chao Feng
提出了一种基于局部稀疏表示的自适应去噪算法。其基本思想是将SR局部应用于嵌入在延迟坐标的高维空间中的信号簇。根据信号的性质，信号簇由原子的稀疏线性组合表示。将该算法应用于含噪混沌信号的去噪，测试其性能。与核主成分分析（Kernel PCA）、局部独立成分分析（Local伊卡）、局部PCA和小波收缩（WS）等主流去噪算法相比，该算法具有更高的去噪效率。
An adaptive denoising algorithm based on local sparse representation (local SR) is proposed. The basic idea is applying SR locally to clusters of signals embedded in a high-dimensional space of delayed coordinates. The clusters of signals are represented by the sparse linear combinations of atoms depending on the nature of the signal. The algorithm is applied to noisy chaotic signals denoising for testing its performance. In comparison with recently reported leading alternative denoising algorithms such as kernel principle component analysis (Kernel PCA), local independent component analysis (local ICA), local PCA, and wavelet shrinkage (WS), the proposed algorithm is more efficient.