Signal Denoising Method Based on Atom Curve Fitting Improved Dictionary Learning

Abstract

Abstract: In signal denoising problems, using K-SVD and other classic dictionary learning algorithm can not effectively eliminate the noise impact. The method made some amendments for classical dictionary learning by applying nonlinear least squares curve fitting and particle swarm optimization. K-SVD algorithm was used to train the dictionary. Nonlinear least-squares approach was used to fit every atom in the dictionary. Particle swarm optimization method was used to solve the sparse representation of the signal. The reconstructed signal was obtained. The experimental results show that, the denoising effects of the proposed method apparently has increased compared with K-SVD and RLS-DLA.

Key words: dictionary learning, denoising, particle swarm optimization, signal reconstruction, curve fitting

CLC Number:

TN911

Gao Bo, Wang Jun, Zhang Gege. Signal Denoising Method Based on Atom Curve Fitting Improved Dictionary Learning[J]. Journal of System Simulation, 2015, 27(12): 2935-2941.

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