Function groups -
fwt2 is designed for 2-dimensional wavelet transformation.
It corresponds mainly to dwt for the one-dimensional case.
If needed it can work with the tensor product
of one dimensional wavelet transforms.
||c = fwt2 (x, l, h, a)
|x ||n x n matrix, input data where n has to be a power of 2
|l ||integer, l^2 is the number of the father wavelet coefficients
|h ||m x 1 vector, wavelet basis
|a ||integer, 0,1,2,3,... see notes
|c ||n x n matrix, resulting coefficients|
In density or regression estimation the input data have to
be realizations on an equispaced grid. The parameter a indicates
symmetry properties of 2 dimensional wavelet transform.
The case a = 0 corresponds to the classical 2 dimensional wavelet transformation.
The case a >= log_2(n) gives the tensor product of one dimensional
To get vectors of the wavelet basis, the library wavelet has to be
h can be either daubechies2,4,6,8,10,12,14,16,18,20, symmlet4 to 10 or
coiflet1 to 5.
; loads the wavelet library
; initializes random generator
; generates some data(line from top left to bottom right)
n = 16
i = 1:n
x = xo+0.2.*normal(n,n)
; computes bivariate wavelet coefficients
c = fwt2(x, 4, daubechies4, 0);
; hard threshold
c = c.*(abs(c).>0.3)
; applies inverse transformation
y = invfwt2(c, 4, daubechies4, 0)
; compares orginal picture with thresholded picture
Content of max
(C) MD*TECH Method and Data Technologies, 05.02.2006