# wdcbm2

Thresholds for wavelet 2-D using Birgé-Massart strategy

## Description

`[`

returns level-dependent thresholds `thr`

,`nkeep`

] = wdcbm2(`C`

,`S`

,`alpha`

,`M`

)`thr`

and numbers of coefficients
to be kept `nkeep`

, for denoising or compressing an image.
`wdcbm2`

uses a wavelet coefficients selection rule based on the
Birgé-Massart strategy to obtain the thresholds.

`[`

is the wavelet
decomposition structure of the image to be denoised or compressed, at level `C`

,`S`

]

. *N* =
size(`S`

,1)-2`alpha`

and
`M`

are real numbers greater than 1.

`wdcbm2(`

is equivalent to
`C`

,`S`

,`alpha`

)`wdcbm2(`

.`C`

,`S`

,`alpha`

,prod(S(1,:)))

## Examples

## Input Arguments

## Output Arguments

## More About

## References

[1] Birgé, Lucien, and Pascal
Massart. “From Model Selection to Adaptive Estimation.” In *Festschrift for
Lucien Le Cam: Research Papers in Probability and Statistics*, edited by
David Pollard, Erik Torgersen, and Grace L. Yang, 55–87. New York, NY: Springer, 1997.
https://doi.org/10.1007/978-1-4612-1880-7_4.

## Version History

**Introduced before R2006a**