The result is a new

DiscreteWaveletData object representing thresholded coefficients:

Specify thresholding method:

Use specific threshold function:

Also specify how to choose threshold value:

Inverse transform and compare:

Visualize the effect on wavelet coefficients:

Compare using

WaveletListPlot:

Perform thresholding on specific wavelet indexes:

By default only the detail coefficients are thresholded:

Use

Automatic to threshold coefficients used in the inverse transform:

Use

All to threshold all coefficients:

Use

to fully control which coefficients to threshold:

Compare the resulting coefficients:

Smooth noisy data using automatic thresholding methods:

Compare all named automatic thresholding methods tspec:

Choose specific thresholding function tfun to apply:

Reconstruct data from thresholded coefficients with automatically chosen threshold value:

Use a named method to automatically compute the threshold value

:

Reconstruct data after

thresholding with various threshold value selection methods:

Use a specific numerical threshold value

:

The best smoothing occurs for threshold values

that are similar to the scale of the noise:

Use a function to compute a threshold value

:

Threshold all coefficients below the standard deviation:

Plot the effect of thresholding function tfun on coefficient values ranging from -0.5 to 0.5:

Apply thresholding separately at each refinement level to data varying on different scales:

Different methods for selecting separate threshold values at each level:

Compare with methods that choose one threshold value for all levels:

Perform thesholding by specifying a function and wavelet index to compute

:

Threshold all detail coefficients below the standard deviation: