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Wavelet filters


[Lo_D,Hi_D,Lo_R,Hi_R] = wfilters('wname')
[F1,F2] = wfilters('wname','type')


[Lo_D,Hi_D,Lo_R,Hi_R] = wfilters('wname') computes four filters associated with the orthogonal or biorthogonal wavelet named in the character vector 'wname'.

The four output filters are

  • Lo_D, the decomposition low-pass filter

  • Hi_D, the decomposition high-pass filter

  • Lo_R, the reconstruction low-pass filter

  • Hi_R, the reconstruction high-pass filter

Available orthogonal or biorthogonal wavelet names 'wname' are listed in the table below.

Wavelet Families



'db1' or 'haar', 'db2', ... ,'db10', ... , 'db45'


'coif1', ... , 'coif5'


'sym2', ... , 'sym8', ... ,'sym45'

Fejer-Korovkin filters

'fk4', 'fk6', 'fk8', 'fk14', 'fk22'

Discrete Meyer



'bior1.1', 'bior1.3', 'bior1.5'
'bior2.2', 'bior2.4', 'bior2.6', 'bior2.8'
'bior3.1', 'bior3.3', 'bior3.5', 'bior3.7'
'bior3.9', 'bior4.4', 'bior5.5', 'bior6.8'

Reverse Biorthogonal

'rbio1.1', 'rbio1.3', 'rbio1.5'
'rbio2.2', 'rbio2.4', 'rbio2.6', 'rbio2.8'
'rbio3.1', 'rbio3.3', 'rbio3.5', 'rbio3.7'
'rbio3.9', 'rbio4.4', 'rbio5.5', 'rbio6.8'

[F1,F2] = wfilters('wname','type') returns the following filters:

Lo_D and Hi_D

(Decomposition filters)

If 'type' = 'd'
Lo_R and Hi_R

(Reconstruction filters)

If 'type' = 'r'
Lo_D and Lo_R

(Low-pass filters)

If 'type' = 'l'
Hi_D and Hi_R

(High-pass filters)

If 'type' = 'h'


% Set wavelet name. 
wname = 'db5';

% Compute the four filters associated with wavelet name given 
% by the input character vector wname. 
[Lo_D,Hi_D,Lo_R,Hi_R] = wfilters(wname); 
subplot(221); stem(Lo_D); 
title('Decomposition low-pass filter'); 
subplot(222); stem(Hi_D); 
title('Decomposition high-pass filter'); 
subplot(223); stem(Lo_R); 
title('Reconstruction low-pass filter'); 
subplot(224); stem(Hi_R); 
title('Reconstruction high-pass filter'); 
xlabel('The four filters for db5')

% Editing some graphical properties,
% the following figure is generated.


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.

Introduced before R2006a

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