hf.bt

hf.bt

hf.bt(x, filter.number = 1, family = "DaubExPhase", min.level = 3, noise.level = NULL)

Denoises a Gaussian contaminated vector using a version of the wavelet-based ``greedy tree" algorithm by Baraniuk, see the reference in the thesis.

Takes:

x The noisy vector, its length must be a power of 2.

filter.number The filter number of the analysing wavelet. Can be set to 1, 2, ..., 10 for family == "DaubExPhase", or to 4, 5, ..., 10 for family == "DaubLeAsymm".

family The family of wavelet bases from which the wavelet filter.number is chosen. Can be set to "DaubExPhase" or "DaubLeAsymm".

min.level The minimum level thresholded.

noise.level Standard deviation of the noise, can be set to a positive number or NULL; in the latter case it will be estimated using MAD.

Returns:

est Denoised version of x.

x	The noisy vector, its length must be a power of 2.
filter.number	The filter number of the analysing wavelet. Can be set to 1, 2, ..., 10 for family == "DaubExPhase", or to 4, 5, ..., 10 for family == "DaubLeAsymm".
family	The family of wavelet bases from which the wavelet filter.number is chosen. Can be set to "DaubExPhase" or "DaubLeAsymm".
min.level	The minimum level thresholded.
noise.level	Standard deviation of the noise, can be set to a positive number or NULL; in the latter case it will be estimated using MAD.