Denoising via truncated sparse decomposition

This paper proposes a denoising algorithm called truncated sparse decomposition(TSD) algorithm,which combines the advantage of the sparse decomposition with that of the minimum energy model truncation operation.Experimental results on two real chaotic signals show that the TSD algorithm outperforms the recently reported denoising algorithms- non-negative sparse coding and singular value decomposition based method.     

An Adaptive Denoising Algorithm for Noisy Chaotic Signals Based on Local Sparse Representation

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.     

Filtering noisy chaotic signal via sparse representation based on random frame dictionary

The denoising problem of impure chaotic signals is addressed in this paper. A method based on sparse representation is proposed, in which the random frame dictionary is generated by a chaotic random search algorithm. The numerical simulation shows the proposed algorithm outperforms those recently reported alternative denoising methods.