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Multilevel 1-D wavelet decomposition


[C,L] = wavedec(X,N,'wname')
[C,L] = wavedec(X,N,Lo_D,Hi_D)


wavedec performs a multilevel one-dimensional wavelet analysis using either a specific wavelet ('wname') or a specific wavelet decomposition filters (Lo_D and Hi_D, see wfilters).


wavedec supports only Type 1 (orthogonal) or Type 2 (biorthogonal) wavelets.

[C,L] = wavedec(X,N,'wname') returns the wavelet decomposition of the signal X at level N, using 'wname'. wavedec does not enforce a maximum level restriction. Use wmaxlev to ensure the wavelet coefficients are free from boundary effects. If boundary effects are not a concern in your application, a good rule is to set N less than or equal to fix(log2(length(X))).

The output decomposition structure contains the wavelet decomposition vector C and the bookkeeping vector L, which contains the number of coefficients by level. The structure is organized as in this level-3 decomposition example.

[C,L] = wavedec(X,N,Lo_D,Hi_D) returns the decomposition structure as above, given the low- and high-pass decomposition filters you specify.


collapse all

The current extension mode for this example is zero-padding, as specified using the dwtmode function.

Load original one-dimensional signal.

load sumsin; 
s = sumsin;

Perform decomposition at level 3 of s using db1. Extract the detail coefficients at levels 1, 2, and 3 from the composition structure.

[c,l] = wavedec(s,3,'db1');
[cd1,cd2,cd3] = detcoef(c,l,[1 2 3]);

Plot the output of the decomposition.

title('Original signal')

title('Level 3 detail coefficients (cd3)')


Given a signal s of length N, the DWT consists of log2 N stages at most. The first step produces, starting from s, two sets of coefficients: approximation coefficients CA1, and detail coefficients CD1. These vectors are obtained by convolving s with the low-pass filter Lo_D for approximation, and with the high-pass filter Hi_D for detail, followed by dyadic decimation (downsampling).

More precisely, the first step is

The length of each filter is equal to 2N. If n = length(s), the signals F and G are of length n + 2N −1 and the coefficients cA1 and cD1 are of length


The next step splits the approximation coefficients cA1 in two parts using the same scheme, replacing s by cA1, and producing cA2 and cD2, and so on

The wavelet decomposition of the signal s analyzed at level j has the following structure: [cAj, cDj, ..., cD1].

This structure contains, for J = 3, the terminal nodes of the following tree:


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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