demo_CreateLPAKernels.m
LPA kernel design
Calculates the LPA convolution smoothing and differentiation
kernels of polynomial approximation and its frequency response characteristic, and draws them.
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![](demoDrawKer1.gif) |
![](demoDrawKer2.gif) |
demo_LPAICI_1D.m
LPA-ICI denoising
Performs the Anisotropic LPA-ICI denoising on observations which are contaminated by additive white Gaussian noise.
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![](demoLPAICI1D.gif) |
![](demoLPAICI1D2.gif) |
demo_MedianICI_1D.m
Median-ICI denoising
Performs the Anisotropic Median-ICI denoising on observations which are contaminated by the additive white Gaussian and impulsive noise.
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![](demoMEDICI1D.gif) |
![](demoMEDICI1D2.gif) |
demo_IdealInvariantScale1D.m
Oracle Invariant scale selection
Illustrates the problem of invariant scale selection. The invariant ideal scale
h of the LPA estimator is found for the noisy signal assuming that the true signal is known.
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![](demoInvScale.gif) |
demo_IdealVaryinigScale1D.m
Oracle Varying scale selection
Illustrates optimal scale selection for every point of the signal.
The varying ideal scale h of the LPA estimator is found for the noisy signal assuming that the true signal is known.
Ideal scale is selected by minimization of mean square error in a point-wise manner.
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![](demoVarScale.gif) |