FWT2 Fast Wavelet Transform 2D
Usage: c = fwt2(f,w,J);
c = fwt2(f,w,J,...);
Input parameters:
f : Input data.
w : Wavelet filter bank definition.
J : Number of filter bank iterations.
Output parameters:
c : Coefficients stored in a matrix.
c=FWT2(f,w,J) returns wavelet coefficients c of the input matrix f*
using J iterations of the basic wavelet filter bank defined by w.
Please see FWT for description of w and J.
FWT2 supports just the non-expansive boundary condition 'per' and
critically subsampled filter banks in order to be able to pack the
coefficients in a matrix. Also the J is limited to some maximum value
for the same reason.
Additional flags make it possible to specify how the algorithm
should subdivide the matrix:
'standard'
Standard behaviour of the JPEG 2000 standard.
This is the default.
'tensor'
This corresponds to doing a full FWT along each dimension of
the matrix.
Examples:
---------
Some simple example of calling the FWT2 function, compare with the
CAMERAMAN image. Only the 70 dB largest coefficients are shown, to
make the structures more visible.
The first example uses the standard layout:
c = fwt2(cameraman,'db8',4);
imagesc(dynlimit(20*log10(abs(c)),70));
axis('image'); colormap(gray);
The second example uses the tensor product layout:
c = fwt2(cameraman,'db8',4,'tensor');
imagesc(dynlimit(20*log10(abs(c)),70));
axis('image'); colormap(gray);
References:
S. Mallat. A wavelet tour of signal processing. Academic Press, San
Diego, CA, 1998.
Url: http://ltfat.github.io/doc/wavelets/fwt2.html
See also: ifwt2, fwtinit, demo_imagecompression.
Package: ltfat