Main Content

Inverse discrete stationary wavelet transform 2-D

`X = iswt2(SWC,`

* 'wname'*)

X = iswt2(A,H,V,D,

`wname`

X = iswt2(A(:,:,end),H,V,D,

`'wname'`

X = iswt2(A(:,:,1,:),H,V,D,

`'wname'`

X = iswt2(SWC,Lo_R,Hi_R)

X = iswt2(A,H,V,D,Lo_R,Hi_R)

X = iswt2(A(:,:,end),H,V,D,Lo_R,Hi_R)

X = iswt2(A(:,:,1,:),H,V,D,

`'wname'`

`iswt2`

performs a multilevel
2-D stationary wavelet reconstruction using either an orthogonal or
a biorthogonal wavelet. Specify the wavelet using its name (* 'wname'*,
see

`wfilters`

for more information)
or its reconstruction filters (`Lo_R`

and `Hi_R`

).`X = iswt2(SWC,`

or
* 'wname'*)

```
X
= iswt2(A,H,V,D,
````wname`

)

reconstructs
the signal `X`

, based on the multilevel stationary wavelet
decomposition structure `SWC`

or `[A,H,V,D]`

(see
`swt2`

).If multilevel stationary wavelet decomposition structure `SWC`

or
`[A,H,V,D]`

was generated from a 2-D matrix, the syntax
`X = iswt2(A(:,:,end),H,V,D,`

reconstructs the signal * 'wname'*)

`X`

.If the stationary wavelet decomposition structure `SWC`

or
`[A,H,V,D]`

was generated from a single level stationary wavelet
decomposition of a 3-D matrix,
`X = iswt2(A(:,:,1,:),H,V,D,`

reconstructs the signal * 'wname'*)

`X`

.`X = iswt2(SWC,Lo_R,Hi_R)`

or
```
X
= iswt2(A,H,V,D,Lo_R,Hi_R)
```

or
`X = iswt2(A(:,:,end),H,V,D,Lo_R,Hi_R)`

or
`X = iswt2(A(:,:,1,:),H,V,D,`

reconstructs as in the previous syntax, using filters that you specify:* 'wname'*)

`Lo_R`

is the reconstruction low-pass filter.`Hi_R`

is the reconstruction high-pass filter.

`Lo_R`

and `Hi_R`

must be
the same length.

**Note**

`iswt2`

synthesizes`X`

from the coefficient arrays generated by`swt2`

.`swt2`

uses double-precision arithmetic internally and returns double-precision coefficient matrices.`swt2`

warns if there is a loss of precision when converting to double.To distinguish a single-level decomposition of a truecolor image from a multilevel decomposition of an indexed image, the approximation and detail coefficient arrays of truecolor images are 4-D arrays. See Distinguish Single-Level Truecolor Image from Multilevel Indexed Image Decompositions. Also see examples Stationary Wavelet Transform of an Image and Inverse Stationary Wavelet Transform of an Image.

If an

`K`

-level decomposition is performed, the dimensions of the`A`

,`H`

,`V`

, and`D`

coefficient arrays are`m`

-by-`n`

-by-3-by-`K`

.If a single-level decomposition is performed, the dimensions of the

`A`

,`H`

,`V`

, and`D`

coefficient arrays are`m`

-by-`n`

-by-1-by-3. Since MATLAB^{®}removes singleton last dimensions by default, the third dimension of the coefficient arrays is singleton.

If SWC or (cA,cH,cV,cD) are obtained from an indexed image analysis
or a truecolor image analysis, then X is an `m`

-by-`n`

matrix
or an `m`

-by-`n`

-by-3 array, respectively.

For more information on image formats, see the `image`

and `imfinfo`

reference
pages.

Nason, G.P.; B.W. Silverman (1995), “The stationary wavelet
transform and some statistical applications,” *Lecture
Notes in Statistics*, 103, pp. 281–299.

Coifman, R.R.; Donoho D.L. (1995), “Translation invariant
de-noising,” *Lecture Notes in Statistics*,
103, pp. 125–150.

Pesquet, J.C.; H. Krim, H. Carfatan (1996), “Time-invariant
orthonormal wavelet representations,” *IEEE Trans.
Sign. Proc.*, vol. 44, 8, pp. 1964–1970.