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Threshold settings manager

`wthrmngr`

returns a global threshold or
level-dependent thresholds for wavelet-based denoising and compression. The
function derives thresholds from the wavelet coefficients in a wavelet
decomposition.

The thresholds are used by Wavelet Toolbox™ denoising and compression tools, such as command-line functions and
the **Wavelet Analyzer** app.

returns the
`thr`

= wthrmngr(`opt`

,`method`

,`C`

,`L`

,`alpha`

)`[`

wavelet decomposition threshold using the sparsity parameter
`C`

,`L`

]`alpha`

. For signals,
`[`

is the output of `C`

,`L`

]`wavedec`

. For
images,
`[`

is the output of `C`

,`L`

]`wavedec2`

.

To learn more about `alpha,`

see `wdcbm`

or
`wdcbm2`

for
compression, and `wbmpen`

for
denoising.

returns the
`thr`

= wthrmngr(`opt`

,`method`

,`C`

,`L`

,`scale`

)`[`

wavelet decomposition threshold using the type of multiplicative
threshold rescaling specified in `C`

,`L`

]`scale`

. For
signals,
`[`

is the output of `C`

,`L`

]`wavedec`

. For
images,
`[`

is the output of `C`

,`L`

]`wavedec2`

.

The `'rigrsure'`

, `'heursure'`

, and
`'minimaxi'`

denoising methods are only
applicable to signals.

To learn more about multiplicative threshold rescaling, see `wden`

.

returns the level-dependent threshold for the stationary wavelet
decomposition, `thr`

= wthrmngr(`opt`

,`method`

,`swtdec`

,`alpha`

)`swtdec`

, of the signal or image to
denoise. `alpha`

specifies the sparsity parameter
(see `wbmpen`

). For
signals, `swtdec`

is the output of `swt`

. For images,
`swtdec`

is the output of `swt2`

.

Thresholds are derived from a subset of the coefficients in the stationary wavelet decomposition. For more information, see Coefficient Selection.

returns the level-dependent threshold for the stationary wavelet
decomposition using the type of multiplicative threshold rescaling
specified in `thr`

= wthrmngr(`opt`

,`method`

,`swtdec`

,`scale`

)`scale`

. For signals,
`swtdec`

is the output of `swt`

. For images,
`swtdec`

is the output of `swt2`

.

Thresholds are derived from a subset of the coefficients in the stationary wavelet decomposition. For more information, see Coefficient Selection.

The `'rigrsure'`

, `'heursure'`

, and
`'minimaxi'`

denoising methods apply only to
signals.

To learn more about multiplicative threshold rescaling, see `wden`

.

returns the global threshold to compress the signal or image,
`thr`

= wthrmngr(`opt`

,'rem_n0',`X`

)`X`

, using the specified wavelet option and
method `'rem_n0'`

.

If `opt`

is `'dw1dcompGBL'`

or
`'dw2dcompGBL'`

, thresholds are based on the
finest-scale wavelet coefficients obtained using the Haar wavelet. If
`opt`

is `'wp1dcompGBL'`

or
`'wp2dcompGBL'`

, thresholds are based on the
finest-scale wavelet packet coefficients obtained using the Haar
wavelet.

To denoise 1-D signals, consider using the

**Wavelet Signal Denoiser**. The app visualizes and denoises real-valued 1-D signals using default parameters. You can also compare results. In addition, you can also recreate the denoised signal in your workspace by generating a MATLAB^{®}script, which uses the`wdenoise`

function.

[1] Birgé, L., and P. Massart. “From Model Selection to Adaptive Estimation.”
*Festschrift for Lucien Le Cam: Research Papers in Probability and
Statistics* (E. Torgersen, D. Pollard, and G. Yang, eds.). New
York: Springer-Verlag, 1997, pp. 55–88.