Apply a wavelet based denoiser.

It transforms each frame from the video input into the wavelet domain, using Cohen-Daubechies-Feauveau 9/7. Then it applies some filtering to the obtained coefficients. It does an inverse wavelet transform after. Due to wavelet properties, it should give a nice smoothed result, and reduced noise, without blurring picture features.

This filter accepts the following options:

threshold

The filtering strength. The higher, the more filtered the video will be. Hard thresholding can use a higher threshold than soft thresholding before the video looks overfiltered. Default value is 2.

method

The filtering method the filter will use.

It accepts the following values:

hard

All values under the threshold will be zeroed.

soft

All values under the threshold will be zeroed. All values above will be reduced by the threshold.

garrote

Scales or nullifies coefficients - intermediary between (more) soft and (less) hard thresholding.

Default is garrote.

nsteps

Number of times, the wavelet will decompose the picture. Picture can’t be decomposed beyond a particular point (typically, 8 for a 640x480 frame - as 2^9 = 512 > 480). Valid values are integers between 1 and 32. Default value is 6.

percent

Partial of full denoising (limited coefficients shrinking), from 0 to 100. Default value is 85.

planes

A list of the planes to process. By default all planes are processed.

type

The threshold type the filter will use.

It accepts the following values:

universal

Threshold used is same for all decompositions.

bayes

Threshold used depends also on each decomposition coefficients.

Default is universal.