Signal denoising by piecewise continuous polynomial fitting
Author
Yıldız, Aykut
Arıkan, Orhan
Date
2010Source Title
2010 IEEE 18th Signal Processing and Communications Applications Conference
Publisher
IEEE
Pages
69 - 72
Language
Turkish
Type
Conference PaperItem Usage Stats
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Abstract
Piecewise smooth signal denoising is cast as a non-linear optimization problem in terms of transition boundaries and a parametric smooth signal family. Optimal transition boundaries for a given number of transitions are obtained by using particle swarm optimization. The piecewise smooth section parameters are obtained as the maximum likelihood estimates conditioned on the optimal transition boundaries. The proposed algorithm is extended to the case where the number of transition boundaries are unknown by sequentially increasing number of sections until the residual error is at the level of noise standard deviation. Performance comparison with the state of the art techniques reveals the important advantages of the proposed technique. ©2010 IEEE.
Keywords
Maximum likelihood estimateNon-linear optimization problems
Optimal transition
Performance comparison
Piecewise smooth
Piecewise-continuous
Polynomial fittings
Residual error
Signal denoising
Standard deviation
State of the art
Transition boundaries
Algorithms
Maximum likelihood estimation
Noise pollution control
Particle swarm optimization (PSO)
Signal processing