C09FCF (PDF version)
C09 Chapter Contents
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NAG Library Manual

NAG Library Routine Document

C09FCF

Note:  before using this routine, please read the Users' Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details.

 Contents

    1  Purpose
    7  Accuracy

1  Purpose

C09FCF computes the three-dimensional multi-level discrete wavelet transform (DWT). The initialization routine C09ACF must be called first to set up the DWT options.

2  Specification

SUBROUTINE C09FCF ( M, N, FR, A, LDA, SDA, LENC, C, NWL, DWTLVM, DWTLVN, DWTLVFR, ICOMM, IFAIL)
INTEGER  M, N, FR, LDA, SDA, LENC, NWL, DWTLVM(NWL), DWTLVN(NWL), DWTLVFR(NWL), ICOMM(260), IFAIL
REAL (KIND=nag_wp)  A(LDA,SDA,FR), C(LENC)

3  Description

C09FCF computes the multi-level DWT of three-dimensional data. For a given wavelet and end extension method, C09FCF will compute a multi-level transform of a three-dimensional array A, using a specified number, nfwd, of levels. The number of levels specified, nfwd, must be no more than the value lmax returned in NWLMAX by the initialization routine C09ACF 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 nfwd, all eight types of coefficients are stored. All coefficients are packed into a single array.

4  References

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

5  Parameters

1:     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 routine C09ACF.
2:     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 routine C09ACF.
3:     FR – INTEGERInput
On entry: the number of two-dimensional frames.
Constraint: this must be the same as the value FR passed to the initialization routine C09ACF.
4:     ALDASDAFR – REAL (KIND=nag_wp) arrayInput
On entry: the m by n by fr three-dimensional input data A, where with Aijk stored in Aijk.
5:     LDA – INTEGERInput
On entry: the first dimension of the array A as declared in the (sub)program from which C09FCF is called.
Constraint: LDAM.
6:     SDA – INTEGERInput
On entry: the second dimension of the array A as declared in the (sub)program from which C09FCF is called.
Constraint: SDAN.
7:     LENC – INTEGERInput
On entry: the dimension of the array C as declared in the (sub)program from which C09FCF is called.
Constraint: LENCnct, where nct is the total number of wavelet coefficients that correspond to a transform with NWL levels.
8:     CLENC – REAL (KIND=nag_wp) arrayOutput
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 C09FYF or C09FZF 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 qi denote the number of coefficients of each type at level i, for i=1,2,,nfwd, such that qi= DWTLVMnfwd-i+1× DWTLVNnfwd-i+1× DWTLVFRnfwd-i+1 . Then, letting k1=qnfwd and kj+1=kj+qnfwd-j/7+1, for j=1,2,,7nfwd, the coefficients are stored in C as follows:
Ci, for i=1,2,,k1
Contains the level nfwd approximation coefficients, anfwd. Note that for computational efficiency reasons these coefficients are stored as DWTLVM1× DWTLVN1× DWTLVFR1  in C.
Ci, for i=kj+1,,kj+1
Contains the level nfwd-j/7+1 detail coefficients. These are:
  • LLH coefficients if j mod 7=1;
  • LHL coefficients if j mod 7=2;
  • LHH coefficients if j mod 7=3;
  • HLL coefficients if j mod 7=4;
  • HLH coefficients if j mod 7=5;
  • HHL coefficients if j mod 7=6;
  • HHH coefficients if j mod 7=0,
for j=1,,7nfwd. 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 DWTLVFR j/7 × DWTLVM j/7 × DWTLVN j/7  in C.
9:     NWL – INTEGERInput
On entry: the number of levels, nfwd, in the multi-level resolution to be performed.
Constraint: 1NWLlmax, where lmax is the value returned in NWLMAX (the maximum number of levels) by the call to the initialization routine C09ACF.
10:   DWTLVMNWL – INTEGER arrayOutput
On exit: the number of coefficients in the first dimension for each coefficient type at each level. DWTLVMi contains the number of coefficients in the first dimension (for each coefficient type computed) at the (nfwd-i+1)th level of resolution, for i=1,2,,nfwd.
11:   DWTLVNNWL – INTEGER arrayOutput
On exit: the number of coefficients in the second dimension for each coefficient type at each level. DWTLVNi contains the number of coefficients in the second dimension (for each coefficient type computed) at the (nfwd-i+1)th level of resolution, for i=1,2,,nfwd.
12:   DWTLVFRNWL – INTEGER arrayOutput
On exit: the number of coefficients in the third dimension for each coefficient type at each level. DWTLVFRi contains the number of coefficients in the third dimension (for each coefficient type computed) at the (nfwd-i+1)th level of resolution, for i=1,2,,nfwd.
13:   ICOMM260 – INTEGER arrayCommunication Array
On entry: contains details of the discrete wavelet transform and the problem dimension as setup in the call to the initialization routine C09ACF.
On exit: contains additional information on the computed transform.
14:   IFAIL – INTEGERInput/Output
On entry: IFAIL must be set to 0, -1​ or ​1. If you are unfamiliar with this parameter you should refer to Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value -1​ or ​1 is recommended. If the output of error messages is undesirable, then the value 1 is recommended. Otherwise, if you are not familiar with this parameter, the recommended value is 0. When the value -1​ or ​1 is used it is essential to test the value of IFAIL on exit.
On exit: IFAIL=0 unless the routine detects an error or a warning has been flagged (see Section 6).

6  Error Indicators and Warnings

If on entry IFAIL=0 or -1, explanatory error messages are output on the current error message unit (as defined by X04AAF).
Errors or warnings detected by the routine:
IFAIL=1
On entry, FR=value.
Constraint: FR=value, the value of FR on initialization (see C09ACF).
On entry, M=value.
Constraint: M=value, the value of M on initialization (see C09ACF).
On entry, N=value.
Constraint: N=value, the value of N on initialization (see C09ACF).
IFAIL=2
On entry, LDA=value and M=value.
Constraint: LDAM.
On entry, SDA=value and N=value.
Constraint: SDAN.
IFAIL=3
On entry, LENC=value.
Constraint: LENCvalue, the total number of coefficents to be generated.
IFAIL=5
On entry, NWL=value.
Constraint: NWL1.
On entry, NWL=value and NWLMAX=value in C09ACF.
Constraint: NWLNWLMAX in C09ACF.
IFAIL=6
Either the communication array ICOMM has been corrupted or there has not been a prior call to the initialization routine C09ACF.
The initialization routine was called with WTRANS='S'.
IFAIL=-99
An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.8 in the Essential Introduction for further information.
IFAIL=-399
Your licence key may have expired or may not have been installed correctly.
See Section 3.7 in the Essential Introduction for further information.
IFAIL=-999
Dynamic memory allocation failed.
See Section 3.6 in the Essential Introduction for further information.

7  Accuracy

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.

8  Parallelism and Performance

Not applicable.

9  Further Comments

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 cij is a detail coefficient then c^ij=cij+σεij and σεij is the transformed noise term. If some threshold parameter α 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 c-ij to be an approximation to the required detail coefficient without noise, cij. The resulting coefficients can then be used as input to C09FDF in order to reconstruct the denoised signal. See Section 10 in C09FZF 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.

10  Example

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 WAVNAM='BIOR1.1' in C09ACF) with periodic end extension, prints a selected set of wavelet coefficients and then reconstructs and verifies that the reconstruction matches the original data.

10.1  Program Text

Program Text (c09fcfe.f90)

10.2  Program Data

Program Data (c09fcfe.d)

10.3  Program Results

Program Results (c09fcfe.r)


C09FCF (PDF version)
C09 Chapter Contents
C09 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2015