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Multilevel 2-D wavelet decomposition


[C,S] = wavedec2(X,N,'wname')
[C,S] = wavedec2(X,N,Lo_D,Hi_D)


wavedec2 is a two-dimensional wavelet analysis function.

[C,S] = wavedec2(X,N,'wname') returns the wavelet decomposition of the matrix X at level N, using the wavelet named in character vector 'wname' (see wfilters for more information).

Outputs are the decomposition vector C and the corresponding bookkeeping matrix S.

N must be a strictly positive integer (see wmaxlev for more information).

Instead of giving the wavelet name, you can give the filters.

For [C,S] = wavedec2(X,N,Lo_D,Hi_D), Lo_D is the decomposition low-pass filter and Hi_D is the decomposition high-pass filter.

Vector C is organized as a vector with A(N), H(N), V(N), D(N), H(N-1), V(N-1), D(N-1), ..., H(1), V(1), D(1), where A, H, V, and D are each a row vector. Each vector is the vector column-wise storage of a matrix.

  • A contains the approximation coefficients

  • H contains the horizontal detail coefficients

  • V contains the vertical detail coefficients

  • D contains the diagonal detail coefficients

Matrix S is such that

  • S(1,:) = size of approximation coefficients(N).

  • S(i,:) = size of detail coefficients(N-i+2) for i = 2, ...N+1 and S(N+2,:) = size(X).


Decomposition Structure

This example shows the structure of wavedec2 output matrices.

Load original image from the woman.mat file, which contains variables named X and map.

load woman;

Get current discrete wavelet transform extension mode.

origMode = dwtmode('status','nodisplay');

Change to periodic boundary handling.

!  WARNING: Change DWT Extension Mode  !
**  DWT Extension Mode: Periodization  **

Perform decomposition at level 2 of X using db1.

[c,s] = wavedec2(X,2,'db1');

Get the decomposition structure organization.

sizex = size(X)
sizex = 

   256   256

sizec = size(c)
sizec = 

           1       65536

Reset discrete wavelet transform extension mode to its original mode.

!  WARNING: Change DWT Extension Mode  !
**  DWT Extension Mode: Symmetrization (half-point)  **

Extract and Display Image Decomposition Level

Extract and display images of wavelet decomposition level details. The resulting images are similar to the visualizations in the At level 2, with haar --> woman indexed image example in the Wavelet 2-D interactive tool. Use waveletAnalyzer to launch this tool.

load woman; 
[H1,V1,D1] = detcoef2('all',c,s,1);
A1 = appcoef2(c,s,'haar',1); 
V1img = wcodemat(V1,255,'mat',1);
H1img = wcodemat(H1,255,'mat',1);
D1img = wcodemat(D1,255,'mat',1);
A1img = wcodemat(A1,255,'mat',1);
[H2,V2,D2] = detcoef2('all',c,s,2);
A2 = appcoef2(c,s,'haar',2); 
V2img = wcodemat(V2,255,'mat',1);
H2img = wcodemat(H2,255,'mat',1);
D2img = wcodemat(D2,255,'mat',1);
A2img = wcodemat(A2,255,'mat',1);
colormap pink(255);
title('Approximation Coef. of Level 1');

title('Horizontal detail Coef. of Level 1');

title('Vertical detail Coef. of Level 1');

title('Diagonal detail Coef. of Level 1');

colormap pink(255);
title('Approximation Coef. of Level 2');

title('Horizontal detail Coef. of Level 2');

title('Vertical detail Coef. of Level 2');

title('Diagonal detail Coef. of Level 2');


When X represents an indexed image, X, as well as the output arrays cA,cH,cV, and cD are m-by-n matrices. When X represents a truecolor image, it is an m-by-n-by-3 array, where each m-by-n matrix represents a red, green, or blue color plane concatenated along the third dimension. The size of vector C and the size of matrix S depend on the type of analyzed image.

For a truecolor image, the decomposition vector C and the corresponding bookkeeping matrix S can be represented as follows.

For more information on image formats, see the image and imfinfo reference pages.


For images, an algorithm similar to the one-dimensional case is possible for two-dimensional wavelets and scaling functions obtained from one-dimensional ones by tensor product.

This kind of two-dimensional DWT leads to a decomposition of approximation coefficients at level j in four components: the approximation at level j+1, and the details in three orientations (horizontal, vertical, and diagonal).

The following chart describes the basic decomposition step for images:

So, for J=2, the two-dimensional wavelet tree has the form


Daubechies, I. (1992), Ten lectures on wavelets, CBMS-NSF conference series in applied mathematics. SIAM Ed.

Mallat, S. (1989), “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Pattern Anal. and Machine Intell., vol. 11, no. 7, pp. 674–693.

Meyer, Y. (1990), Ondelettes et opérateurs, Tome 1, Hermann Ed. (English translation: Wavelets and operators, Cambridge Univ. Press. 1993.)

Extended Capabilities

Introduced before R2006a

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