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appcoef2

2-D approximation coefficients extraction

Calling Sequence

A=appcoef2(C,S,wname,[N])
A=appcoef2(C,S,Lo_R,Hi_R,[N])

Parameters

wname :

wavelet name, haar( "haar"), daubechies ("db1" to "db36"), coiflets ("coif1" to "coif17"), symlets ("sym2" to "sym20"), legendre ("leg1" to "leg9"), bathlets("bath4.0" to "bath4.15" and "bath6.0" to "bath6.15"), dmey ("dmey"), beyklin ("beylkin"), vaidyanathan ("vaidyanathan"), biorthogonal B-spline wavelets ("bior1.1" to "bior6.8"), "rbior1.1" to "rbior6.8"

A :

extracted approximation coefficients

Lo_R :

lowpass synthesis filter

Hi_R :

highpass syntheis filter

C :

coefficent array

S :

size array

N :

restruction level with N<=length(L)-2

Description

appcoef2 can be used for extraction or reconstruction of approximation coeffient at level N after a multiple level decompostion. Extension mode is stored as a global variable and could be changed with dwtmode. If N is omitted, the maximum level (length(L)-2) is used.

The length of A depends on the level N.

C and L can be generated using wavedec2.

Examples

load(get_swt_path()+"demos/image/woman.dat");
[C,S]=wavedec2(X,3,'db2');
A1=appcoef2(C,S,'db2',1);
A2=appcoef2(C,S,'db2',2);
A3=appcoef2(C,S,'db2',3);
scf();clf();
f=gcf();f.color_map=gray(256);
subplot(221)
Matplot(X);
a=gca();a.tight_limits="on";
title("original");
subplot(222)
Matplot(A1);
a=gca();a.tight_limits="on";
title("approximation level 1");
subplot(223)
Matplot(A2);
a=gca();a.tight_limits="on";
title("approximation level 2");
subplot(224)
Matplot(A3);
a=gca();a.tight_limits="on";
title("approximation level 3");

Authors

See also


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