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1-D wavelet decomposition

wavedec performs a multilevel one-dimensional wavelet analysis using either a specific wavelet or a specific pair of wavelet decomposition filters.


[c,l] = wavedec(x,n,wname)
[c,l] = wavedec(x,n,LoD,HiD)



[c,l] = wavedec(x,n,wname) returns the wavelet decomposition of the signal x at level n using the wavelet wname. The output decomposition structure consists of 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 diagram.

[c,l] = wavedec(x,n,LoD,HiD) returns the wavelet decomposition using the specified lowpass, and highpass wavelet decomposition filters LoD and HiD.


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Load and plot a one-dimensional signal.

load sumsin 

Perform a 3-level wavelet decomposition of the signal using the order 2 Daubechies wavelet. Extract the coarse scale approximation coefficients and the detail coefficients from the decomposition.

[c,l] = wavedec(sumsin,3,'db2');
approx = appcoef(c,l,'db2');
[cd1,cd2,cd3] = detcoef(c,l,[1 2 3]);

Plot the coefficients.

title('Approximation Coefficients')
title('Level 3 Detail Coefficients')
title('Level 2 Detail Coefficients')
title('Level 1 Detail Coefficients')

Input Arguments

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Input signal, specified as a real-valued vector.

Data Types: double

Level of decomposition, specified as a positive integer. wavedec does not enforce a maximum level restriction. Use wmaxlev to ensure that 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))).

Data Types: double

Analyzing wavelet, specified as a character vector or string scalar.


wavedec supports only Type 1 (orthogonal) or Type 2 (biorthogonal) wavelets. See wfilters for a list of orthogonal and biorthogonal wavelets.

Wavelet decomposition lowpass filter, specified as an even-length real-valued vector. LoD must be of the same length as HiD. See wfilters for details.

Wavelet decomposition highpass filter, specified as an even-length real-valued vector. HiD must be of the same length as LoD. See wfilters for details.

Output Arguments

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Wavelet decomposition vector, returned as a real-valued vector. The bookkeeping vector l contains the number of coefficients by level.

Bookkeeping vector, returned as a vector of positive integers. The bookkeeping vector is used to parse the coefficients in the wavelet decomposition vector c by level.


Given a signal s of length N, the DWT consists of at most log2 N steps. Starting from s, the first step produces two sets of coefficients: approximation coefficients cA1 and detail coefficients cD1. Convolving s with the lowpass filter LoD and the highpass filter HiD, followed by dyadic decimation (downsampling), results in the approximation and detail coefficients respectively.


  • — Convolve with filter X

  • 2 — Downsample (keep the even-indexed elements)

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:


[1] Daubechies, I. Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics. Philadelphia, PA: SIAM Ed, 1992.

[2] Mallat, S. G. “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 11, Issue 7, July 1989, pp. 674–693.

[3] Meyer, Y. Wavelets and Operators. Translated by D. H. Salinger. Cambridge, UK: Cambridge University Press, 1995.

Extended Capabilities

Introduced before R2006a