1-D wavelet decomposition
returns the wavelet decomposition of the 1-D signal
l] = wavedec(
x at level
n using the wavelet
wname. The output
decomposition structure consists of the wavelet decomposition vector
c and the bookkeeping vector
contains the number of coefficients by level. The structure is organized as in this
level-3 decomposition diagram.
Load and plot a one-dimensional signal.
load sumsin plot(sumsin) title('Signal')
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.
subplot(4,1,1) plot(approx) title('Approximation Coefficients') subplot(4,1,2) plot(cd3) title('Level 3 Detail Coefficients') subplot(4,1,3) plot(cd2) title('Level 2 Detail Coefficients') subplot(4,1,4) plot(cd1) title('Level 1 Detail Coefficients')
x— Input signal
Input signal, specified as a real-valued vector.
n— Level of decomposition
Level of decomposition, specified as a positive integer.
wavedec does not enforce a maximum level
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
wname— Analyzing wavelet
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.
LoD,HiD— Wavelet decomposition filters
Wavelet decomposition filters, specified as a pair of even-length
LoD is the lowpass decomposition
HiD is the highpass decomposition filter. The
HiD must be equal.
wfilters for additional
c— Wavelet decomposition vector
Wavelet decomposition vector, returned as a real-valued vector. The
l contains the number of
coefficients by level.
l— Bookkeeping vector
Bookkeeping vector, returned as a vector of positive integers. The
bookkeeping vector is used to parse the coefficients in the wavelet
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
— 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:
 Daubechies, I. Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics. Philadelphia, PA: SIAM Ed, 1992.
 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.
 Meyer, Y. Wavelets and Operators. Translated by D. H. Salinger. Cambridge, UK: Cambridge University Press, 1995.
Usage notes and limitations:
Variable-size data support must be enabled.
wname must be constant.