# iswt2

Inverse discrete stationary 2-D wavelet transform

## Description

returns the inverse discrete stationary 2-D wavelet transform of the wavelet
decomposition `X`

= iswt2(`swc`

,`wname`

)`swc`

using the wavelet
`wname`

. The decomposition `swc`

is
the output of `swt2`

.

**Note**

`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.

uses the approximation coefficients array `X`

= iswt2(`A`

,`H,V,D`

,`wname`

)`A`

and detail
coefficient arrays `H`

, `V`

, and
`D`

. The arrays `H`

, `V`

,
and `D`

contain the horizontal, vertical, and diagonal detail
coefficients, respectively. The arrays are the output of `swt2`

.

If the decomposition

`swc`

or the coefficient arrays`A`

,`H`

,`V`

, and`D`

were generated from a multilevel decomposition of a 2-D matrix, the syntax`X = iswt2(A(:,:,end),H,V,D,wname)`

reconstructs the 2-D matrix.If the decomposition

`swc`

or the coefficient arrays`A`

,`H`

,`V`

, and`D`

were generated from a single-level decomposition of a 3-D array, the syntax`X = iswt2(A(:,:,1,:),H,V,D,wname)`

reconstructs the 3-D array.

uses the lowpass and highpass wavelet reconstruction filters
`X`

= iswt2(`A`

,`H,V,D`

,`LoR,HiR`

)`LoR`

and `HiR`

, respectively.

If the decomposition

`swc`

or the coefficient arrays`A`

,`H`

,`V`

, and`D`

were generated from a multilevel decomposition of a 2-D matrix, the syntax`X = iswt2(A(:,:,end),H,V,D,LoR,HiR)`

reconstructs the 2-D matrix.If the decomposition

`swc`

or the coefficient arrays`A`

,`H`

,`V`

, and`D`

were generated from a single-level decomposition of a 3-D array, the syntax`X = iswt2(A(:,:,1,:),H,V,D,LoR,HiR)`

reconstructs the 3-D array.

## Examples

## Input Arguments

## Output Arguments

## References

[1] Nason, G. P., and B. W.
Silverman. “The Stationary Wavelet Transform and Some Statistical Applications.” In
*Wavelets and Statistics*, edited by Anestis Antoniadis and
Georges Oppenheim, 103:281–99. New York, NY: Springer New York, 1995.
https://doi.org/10.1007/978-1-4612-2544-7_17.

[2] Coifman, R. R., and D. L.
Donoho. “Translation-Invariant De-Noising.” In *Wavelets and
Statistics*, edited by Anestis Antoniadis and Georges Oppenheim,
103:125–50. New York, NY: Springer New York, 1995.
https://doi.org/10.1007/978-1-4612-2544-7_9.

[3] Pesquet, J.-C., H. Krim, and
H. Carfantan. “Time-Invariant Orthonormal Wavelet Representations.” *IEEE
Transactions on Signal Processing* 44, no. 8 (August 1996): 1964–70.
https://doi.org/10.1109/78.533717.

## Extended Capabilities

## Version History

**Introduced before R2006a**