Estimate noise of 1-D wavelet coefficients

`STDC = wnoisest(C,L,S)`

STDC
= wnoisest(C)

STDC =
wnoisest(C)

`STDC = wnoisest(C,L,S)`

returns estimates
of the detail coefficients' standard deviation for levels contained
in the input vector `S`

. `[C,L]`

is
the input wavelet decomposition structure (see `wavedec`

for more information).

If `C`

is a one dimensional cell array, ```
STDC
= wnoisest(C)
```

returns a vector such that `STDC(k)`

is
an estimate of the standard deviation of `C{k}`

.

If `C`

is a numeric array, ```
STDC =
wnoisest(C)
```

returns a vector such that `STDC(k)`

is
an estimate of the standard deviation of `C(k,:)`

.

The estimator used is Median Absolute Deviation / 0.6745, well
suited for zero mean Gaussian white noise in the de-noising one-dimensional
model (see `thselect`

for more
information).

Donoho, D.L.; I.M. Johnstone (1994), "Ideal spatial adaptation
by wavelet shrinkage," *Biometrika*, vol
81, pp. 425–455.

Donoho, D.L.; I.M. Johnstone (1995), "Adapting to unknown
smoothness via wavelet shrinkage via wavelet shrinkage," *JASA*,
vol 90, 432, pp. 1200–1224.

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