A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolutionary-based methods for adaptive signal representation
Signal Processing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Local feature extraction and its applications using a library of bases
Local feature extraction and its applications using a library of bases
Combining mutation operators in evolutionary programming
IEEE Transactions on Evolutionary Computation
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors
IEEE Transactions on Information Theory
Universal coding, information, prediction, and estimation
IEEE Transactions on Information Theory
Bayesian wavelet denoising and evolutionary calibration
Digital Signal Processing
Convolution wavelet packet transform and its applications to signal processing
Digital Signal Processing
Genetic wavelet packets for speech recognition
Expert Systems with Applications: An International Journal
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In recent years several wavelet thresholding schemes for denoising have been proposed. However, thresholding rules depend heavily on the choice of the parameters. Wavelet thresholding rules are typically nonlinear, leading to nonconvex target functions. In this paper, we present an evolutionary computation approach for parameter elicitation in penalized wavelet models. We begin with parameter models for global hard- and soft-thresholding. Then, we extend the methodology for parameter elicitation in two directions: block thresholding and best-basis denoising. The proposed evolutionary approach enables the joint optimization of wavelet basis selection and thresholding parameters for signal denoising. Numerical simulations are used to illustrate the proposed methodology and compare the behavior of the various denoising procedures.