# NAG Library Function Document

## 1Purpose

nag_mldwt_3d (c09fcc) computes the three-dimensional multi-level discrete wavelet transform (DWT). The initialization function nag_wfilt_3d (c09acc) must be called first to set up the DWT options.

## 2Specification

 #include #include
 void nag_mldwt_3d (Integer m, Integer n, Integer fr, const double a[], Integer lda, Integer sda, Integer lenc, double c[], Integer nwl, Integer dwtlvm[], Integer dwtlvn[], Integer dwtlvfr[], Integer icomm[], NagError *fail)

## 3Description

nag_mldwt_3d (c09fcc) computes the multi-level DWT of three-dimensional data. For a given wavelet and end extension method, nag_mldwt_3d (c09fcc) will compute a multi-level transform of a three-dimensional array $A$, using a specified number, ${n}_{\mathrm{fwd}}$, of levels. The number of levels specified, ${n}_{\mathrm{fwd}}$, must be no more than the value ${l}_{\mathrm{max}}$ returned in nwlmax by the initialization function nag_wfilt_3d (c09acc) for the given problem. The transform is returned as a set of coefficients for the different levels (packed into a single array) and a representation of the multi-level structure.
The notation used here assigns level $0$ to the input data, $A$. Level 1 consists of the first set of coefficients computed: the seven sets of detail coefficients are stored at this level while the approximation coefficients are used as the input to a repeat of the wavelet transform at the next level. This process is continued until, at level ${n}_{\mathrm{fwd}}$, all eight types of coefficients are stored. All coefficients are packed into a single array.

## 4References

Wang Y, Che X and Ma S (2012) Nonlinear filtering based on 3D wavelet transform for MRI denoising URASIP Journal on Advances in Signal Processing 2012:40

## 5Arguments

1:    $\mathbf{m}$IntegerInput
On entry: the number of rows of each two-dimensional frame.
Constraint: this must be the same as the value m passed to the initialization function nag_wfilt_3d (c09acc).
2:    $\mathbf{n}$IntegerInput
On entry: the number of columns of each two-dimensional frame.
Constraint: this must be the same as the value n passed to the initialization function nag_wfilt_3d (c09acc).
3:    $\mathbf{fr}$IntegerInput
On entry: the number of two-dimensional frames.
Constraint: this must be the same as the value fr passed to the initialization function nag_wfilt_3d (c09acc).
4:    $\mathbf{a}\left[\mathit{dim}\right]$const doubleInput
Note: the dimension, dim, of the array a must be at least ${\mathbf{lda}}×{\mathbf{sda}}×{\mathbf{fr}}$.
On entry: the $m$ by $n$ by $\mathit{fr}$ three-dimensional input data $A$, where with ${A}_{ijk}$ stored in ${\mathbf{a}}\left[\left(k-1\right)×{\mathbf{lda}}×{\mathbf{sda}}+\left(j-1\right)×{\mathbf{lda}}+i-1\right]$.
5:    $\mathbf{lda}$IntegerInput
On entry: the stride separating row elements of each of the sets of frame coefficients in the three-dimensional data stored in a.
Constraint: ${\mathbf{lda}}\ge {\mathbf{m}}$.
6:    $\mathbf{sda}$IntegerInput
On entry: the stride separating corresponding coefficients of consecutive frames in the three-dimensional data stored in a.
Constraint: ${\mathbf{sda}}\ge {\mathbf{n}}$.
7:    $\mathbf{lenc}$IntegerInput
On entry: the dimension of the array c.
Constraint: ${\mathbf{lenc}}\ge {n}_{\mathrm{ct}}$, where ${n}_{\mathrm{ct}}$ is the total number of wavelet coefficients that correspond to a transform with nwl levels.
8:    $\mathbf{c}\left[{\mathbf{lenc}}\right]$doubleOutput
On exit: the coefficients of the discrete wavelet transform. If you need to access or modify the approximation coefficients or any specific set of detail coefficients then the use of nag_wav_3d_coeff_ext (c09fyc) or nag_wav_3d_coeff_ins (c09fzc) is recommended. For completeness the following description provides details of precisely how the coefficients are stored in c but this information should only be required in rare cases.
Let $q\left(\mathit{i}\right)$ denote the number of coefficients of each type at level $\mathit{i}$, for $\mathit{i}=1,2,\dots ,{n}_{\mathrm{fwd}}$, such that $q\left(i\right)={\mathbf{dwtlvm}}\left[{n}_{\mathrm{fwd}}-i\right]×{\mathbf{dwtlvn}}\left[{n}_{\mathrm{fwd}}-i\right]×{\mathbf{dwtlvfr}}\left[{n}_{\mathrm{fwd}}-i\right]$. Then, letting ${k}_{1}=q\left({n}_{\mathrm{fwd}}\right)$ and ${k}_{\mathit{j}+1}={k}_{\mathit{j}}+q\left({n}_{\mathrm{fwd}}-⌈\mathit{j}/7⌉+1\right)$, for $\mathit{j}=1,2,\dots ,7{n}_{\mathrm{fwd}}$, the coefficients are stored in c as follows:
${\mathbf{c}}\left[\mathit{i}-1\right]$, for $\mathit{i}=1,2,\dots ,{k}_{1}$
Contains the level ${n}_{\mathrm{fwd}}$ approximation coefficients, ${a}_{{n}_{\mathrm{fwd}}}$. Note that for computational efficiency reasons these coefficients are stored as ${\mathbf{dwtlvm}}\left[0\right]×{\mathbf{dwtlvn}}\left[0\right]×{\mathbf{dwtlvfr}}\left[0\right]$ in c.
${\mathbf{c}}\left[\mathit{i}-1\right]$, for $\mathit{i}={k}_{j}+1,\dots ,{k}_{j+1}$
Contains the level ${n}_{\mathrm{fwd}}-⌈j/7⌉+1$ detail coefficients. These are:
• LLH coefficients if ;
• LHL coefficients if ;
• LHH coefficients if ;
• HLL coefficients if ;
• HLH coefficients if ;
• HHL coefficients if ;
• HHH coefficients if ,
for $j=1,\dots ,7{n}_{\mathrm{fwd}}$. See Section 2.1 in the c09 Chapter Introduction for a description of how these coefficients are produced.
Note that for computational efficiency reasons these coefficients are stored as ${\mathbf{dwtlvfr}}\left[⌈j/7⌉-1\right]×{\mathbf{dwtlvm}}\left[⌈j/7⌉-1\right]×{\mathbf{dwtlvn}}\left[⌈j/7⌉-1\right]$ in c.
9:    $\mathbf{nwl}$IntegerInput
On entry: the number of levels, ${n}_{\mathrm{fwd}}$, in the multi-level resolution to be performed.
Constraint: $1\le {\mathbf{nwl}}\le {l}_{\mathrm{max}}$, where ${l}_{\mathrm{max}}$ is the value returned in nwlmax (the maximum number of levels) by the call to the initialization function nag_wfilt_3d (c09acc).
10:  $\mathbf{dwtlvm}\left[{\mathbf{nwl}}\right]$IntegerOutput
On exit: the number of coefficients in the first dimension for each coefficient type at each level. ${\mathbf{dwtlvm}}\left[\mathit{i}-1\right]$ contains the number of coefficients in the first dimension (for each coefficient type computed) at the (${n}_{\mathrm{fwd}}-\mathit{i}+1$)th level of resolution, for $\mathit{i}=1,2,\dots ,{n}_{\mathrm{fwd}}$.
11:  $\mathbf{dwtlvn}\left[{\mathbf{nwl}}\right]$IntegerOutput
On exit: the number of coefficients in the second dimension for each coefficient type at each level. ${\mathbf{dwtlvn}}\left[\mathit{i}-1\right]$ contains the number of coefficients in the second dimension (for each coefficient type computed) at the (${n}_{\mathrm{fwd}}-\mathit{i}+1$)th level of resolution, for $\mathit{i}=1,2,\dots ,{n}_{\mathrm{fwd}}$.
12:  $\mathbf{dwtlvfr}\left[{\mathbf{nwl}}\right]$IntegerOutput
On exit: the number of coefficients in the third dimension for each coefficient type at each level. ${\mathbf{dwtlvfr}}\left[\mathit{i}-1\right]$ contains the number of coefficients in the third dimension (for each coefficient type computed) at the (${n}_{\mathrm{fwd}}-\mathit{i}+1$)th level of resolution, for $\mathit{i}=1,2,\dots ,{n}_{\mathrm{fwd}}$.
13:  $\mathbf{icomm}\left[260\right]$IntegerCommunication Array
On entry: contains details of the discrete wavelet transform and the problem dimension as setup in the call to the initialization function nag_wfilt_3d (c09acc).
On exit: contains additional information on the computed transform.
14:  $\mathbf{fail}$NagError *Input/Output
The NAG error argument (see Section 3.7 in How to Use the NAG Library and its Documentation).

## 6Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
On entry, argument $〈\mathit{\text{value}}〉$ had an illegal value.
NE_INITIALIZATION
Either the communication array icomm has been corrupted or there has not been a prior call to the initialization function nag_wfilt_3d (c09acc).
The initialization function was called with ${\mathbf{wtrans}}=\mathrm{Nag_SingleLevel}$.
NE_INT
On entry, ${\mathbf{fr}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{fr}}=〈\mathit{\text{value}}〉$, the value of fr on initialization (see nag_wfilt_3d (c09acc)).
On entry, ${\mathbf{m}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{m}}=〈\mathit{\text{value}}〉$, the value of m on initialization (see nag_wfilt_3d (c09acc)).
On entry, ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{n}}=〈\mathit{\text{value}}〉$, the value of n on initialization (see nag_wfilt_3d (c09acc)).
On entry, ${\mathbf{nwl}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{nwl}}\ge 1$.
NE_INT_2
On entry, ${\mathbf{lda}}=〈\mathit{\text{value}}〉$ and ${\mathbf{m}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{lda}}\ge {\mathbf{m}}$.
On entry, ${\mathbf{lenc}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{lenc}}\ge 〈\mathit{\text{value}}〉$, the total number of coefficents to be generated.
On entry, ${\mathbf{nwl}}=〈\mathit{\text{value}}〉$ and ${\mathbf{nwlmax}}=〈\mathit{\text{value}}〉$ in nag_wfilt_3d (c09acc).
Constraint: ${\mathbf{nwl}}\le {\mathbf{nwlmax}}$ in nag_wfilt_3d (c09acc).
On entry, ${\mathbf{sda}}=〈\mathit{\text{value}}〉$ and ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{sda}}\ge {\mathbf{n}}$.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 2.7.6 in How to Use the NAG Library and its Documentation for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.

## 7Accuracy

The accuracy of the wavelet transform depends only on the floating-point operations used in the convolution and downsampling and should thus be close to machine precision.

## 8Parallelism and Performance

nag_mldwt_3d (c09fcc) is not threaded in any implementation.

The example program shows how the wavelet coefficients at each level can be extracted from the output array c. Denoising can be carried out by applying a thresholding operation to the detail coefficients at every level. If ${c}_{ij}$ is a detail coefficient then ${\stackrel{^}{c}}_{ij}={c}_{ij}+\sigma {\epsilon }_{ij}$ and $\sigma {\epsilon }_{ij}$ is the transformed noise term. If some threshold parameter $\alpha$ is chosen, a simple hard thresholding rule can be applied as
 $c- ij = 0, if ​ c^ij ≤ α c^ij , if ​ c^ij > α,$
taking ${\stackrel{-}{c}}_{ij}$ to be an approximation to the required detail coefficient without noise, ${c}_{ij}$. The resulting coefficients can then be used as input to nag_imldwt_3d (c09fdc) in order to reconstruct the denoised signal. See Section 10 in nag_wav_3d_coeff_ins (c09fzc) for a simple example of denoising.
See the references given in the introduction to this chapter for a more complete account of wavelet denoising and other applications.

## 10Example

This example computes the three-dimensional multi-level discrete wavelet decomposition for $7×6×5$ input data using the biorthogonal wavelet of order $1.1$ (set ${\mathbf{wavnam}}=\mathrm{Nag_Biorthogonal1_1}$ in nag_wfilt_3d (c09acc)) with periodic end extension, prints a selected set of wavelet coefficients and then reconstructs and verifies that the reconstruction matches the original data.

### 10.1Program Text

Program Text (c09fcce.c)

### 10.2Program Data

Program Data (c09fcce.d)

### 10.3Program Results

Program Results (c09fcce.r)

© The Numerical Algorithms Group Ltd, Oxford, UK. 2017