# Toolbox Alpert Transform - A toolbox to perform the Fast Multiwavelet Alpert Transform

## Setting-up the path and compiling the mex files.

We add some directories to the path.

```path(path, 'toolbox/');
```

## Introduction to the Alpert Multiwavelets

The Alpert transform is a multiwavelets transform based on orthogonal polynomials. It was originally designed for the resolution of partial differential and integral equations, since it avoids boundary artifact and can be used with an arbitrary sampling.

The reference for the numerical algorithm:

• Bradley K. Alpert, Wavelets and Other Bases for Fast Numerical Linear Algebra, in Wavelets: A Tutorial in Theory and Applications, C. K. Chui, editor, Academic Press, New York, 1992.

And more theoretical (continuous setting):

• B.K. Alpert, A class of bases in L^2 for the sparse representatiion of integral operators, in SIAM J. Math. Anal., 24 (1993), 246-262.

The strengh of this transform is that you can transform data sampled irregularly. Of course this algorithm runs in linear time, i.e. O(n). The use of multiwavelets remove any boundary artifact (which are common with wavelet of support > 1, e.g. Daubechies wavelets), but the price to pay is that the wavelets functions are not continuous, they look like the Haar basis functions. So do not use this transform to compress data that will be seen by human eyes (although the reconstruction error can be very low, the reconstructed function can have some ugly steps-like artifacts).

In this toolbox, you can transform a signal (1D, 2D, nD) with arbitrary length and arbitrary sampling (you must each time provide sampling locations in a parameter pos).

The number of vanishing moments (which is also the degree of the polynomial approximation+1) is set via the parameter 'k' (default=3). You can provide different numbers of vanishing moments for X and Y axis, using k=[kx,ky] (default k=[3,3]).

Dichotomic Grouping Functions: All the transforms require to recursively split the data. For each transform a default splitting rule is used, but you can provide your own partition via a parameter part. The way you split the data (isotropicaly, using a prefered direction, etc) will lead to various behaviour for the transform (of course this is relevant for 2D transform only, since the splitting rule in 1D is always the same). The function to perform an automatic grouping is 'dichotomic_grouping' (it can provide orthogonal split along the axis or isotropic using k-means).

## 1D Alper transform at regularly sampled locations

The Alpert transform act just like a traditional wavelet transform (see my Wavelets Toolbox for more details). It is an orthogonal transform, which wavelet of minimal support (just like the haar transform) and with as many vanishing moments as desired. The transform algorithm is O(n), which is similar to the traditional wavelet transform. The main drawback is that the basis wavelet functions are not regular, which might lead to visually deapleasant artifacts after approximation or compression of the signal.

```n = 512;
```

Then we perform the forward transform.

```% sampling location
x = (0:n-1)/n;
% number of vanishing moments
k = 4;
[fw,info] = perform_alpert_transform_1d(f, x, k, +1);
```

To perform approximation, we discard the smallest amplitude coefficients, and then reconstruct.

```fwT = keep_biggest(fw, round(.1*n) );
f1 = perform_alpert_transform_1d(fwT, x, k, -1);
```

For the display, we reverse the coefficients using fw(end:-1:1), so that coarse scale coefficients are located on the left. Each vertical red dashed line is the separation between two wavelet scales.

```clf;
subplot(3,1,1);
plot(f); axis tight; title('Signal');
subplot(3,1,2);
plot_wavelet(fw(end:-1:1), 1); axis([1 n -.3 .3]); title('Coefficients');
subplot(3,1,3);
plot(f1); axis tight; title('Approximated');
``` You can display the Alpert basis vectors by taking the inverse transform of a dirac. For each scale and each positon, there are k Alpert wavelets, this is why this basis is called a multiwavelet basis.

```jlist = [2 3 4];
klist = 1:k;
clf;
for a=1:length(jlist)
f = zeros(n,k);
lgd = {};
for b=1:length(klist)
% construct a diract at the correct location
t = klist(b); j = jlist(a);
lgd{end+1} = ['k=' num2str(t)];
fw = zeros(n,1);
I = find( info.l==j & info.k==t );
fw(I(round(length(I)/2))) = 1;
% inverse transform
f(:,b) = perform_alpert_transform_1d(fw, x, k, -1);
end
% display
subplot( length(jlist), 1, a);
plot(f); axis tight;
title(['j=' num2str(j)]); legend(lgd)
end
``` ## 1D Alpert transform on irrregular samples

The Alpert transform can be used to process signal irregularely sampled. The vectors keep their properties (minimal support, vanishing moments, orthogonality) even on this setting, which is a major advantage of this transform, and makes it unique among wavelet transform.

```% generate regular and random sampling
xreg = (0:n-1)/n;
xireg = rand(n,1).^3; xireg = sort(rescale(xireg));
% generate a piecewise smooth signal for this sampling
f = mod(xireg.^2,.2);
k = 3; % number of VM
fw0 = perform_alpert_transform_1d(f, xreg, k, +1);
fw1 = perform_alpert_transform_1d(f, xireg, k, +1);
```

A comparison of the coefficients with correct and wrong sampling reveals that the coefficients with correct sampling are zero away from singularities. This is because f is a piecewise second degree polynomial, and the Alpert basis has 3 vanishing moments.

```clf; eta = .15;
subplot(3,1,1);
plot(xireg, f, '.-'); axis tight; title('Signal and sampling');
subplot(3,1,2);
plot_wavelet(fw0(end:-1:1),1); axis([1 n -eta eta]); title('Coefficient with (wrong) uniform sampling');
subplot(3,1,3);
plot_wavelet(fw1(end:-1:1),1); axis([1 n -eta eta]); title('Coefficient with (correct) non-uniform sampling');
``` ## 2D Alpert transform

Use function perform_alpert_transform_2d. The default spliting rule for this transform use the X axis (but you can change). So by default, it is not a fully 2D wavelet construction (the transform is pyramidal only on 1 dimension, on the other this is just a fixed polynomial basis with a given number of slices). But if you provide your own subdivision (via parameters part for user-defined or part_type for automatic) then the transform can become isotropic (for example set part_type='kmeans').

Generate a random 2D sampling

```n = 20000;
pos = rand(2,n);
```

Compute a 2D signal by evaluating Lena image at the sampling locations

```n0 = 256;
M = rescale(crop(M,n0));
x = linspace(0,1,n0);
[X,Y] = meshgrid(x,x);
f = interp2(X,Y,M,pos(2,:),pos(1,:));
```

Display the image, its non-uniform sampling, and the linear interpolation.

```% linear interpolation
F  = griddata(pos(2,:),pos(1,:),f, X,Y);
% display
clf;
subplot(1,2,1);
hold on;
imagesc(x,x,M); axis image; axis off; axis ij;
title('Image and sampling (sub-set)');
plot(pos(1,1:1000),pos(2,1:1000), 'r.');
hold off;
subplot(1,2,2);
imageplot(F, 'Interpolated');
``` Compute the 2D Alpert transform.

```k = [2 2]; % vanishing moments
options.part_type = '2axis'; % partitionning type
[fw,info] = perform_alpert_transform_2d(f, pos, k, +1, options);
```

Approximate the coefficients by thresholding, and display on a regular grid the data using interpolation.

```% approximate
fwT = keep_biggest(fw, round(.1*n) );
% inverse transform
[f1,info] = perform_alpert_transform_2d(fwT, pos, k, -1, options);
% interpolate
F1 = griddata(pos(2,:),pos(1,:),f1,X,Y);
% display
clf;
imageplot({F clamp(F1)}, {'Original' 'Approximated'});
``` ## nD and more exotic transforms

Use the function 'perform_alpert_transform_nd' to transform data in arbitrary dimension. The clustering uses function 'dichotomic_grouping'. This transform can also be used to transform data lying on a manifold embedded in R^d. For instance, the function 'test_spherical' gives an example which transform data lying on a sphere.

We first make a random sampling on the sphere and compute a function defined on this sampling.

```n = 2048;
k = 3;
pos = randn(3,n);
pos = pos ./ repmat( sqrt(sum(pos.^2,1)), [3 1]);
f = rescale( pos(1,:).^2 - pos(2,:).*pos(3,:) );
f = cos(10*f(:));
% add a discontinuity to make the approximation problem harder
f = rescale(f - rescale(pos(3,:)'>0) );
```

Then we perform iterative clustering to define hierachical sets of embedded points on the sphere.

```clear options;
options.ptmax = k^2;
options.part_type = 'kmeans';
[part,B,G] = dichotomic_grouping(pos,options);
% display
clf;
subplot(1,2,1);
plot_spherical_partition(pos,part);
title('Clustering of sampling locations');
subplot(1,2,2);
plot_spherical_function(pos,f);
title('Function to transform (interpolated)');
``` Now we can perform the Alpert transform of the function using this specific spherical sampling.

```[fw,info,part] = perform_alpert_transform_nd(f, pos, k, 1, options);
```

Approximation is perform by simply thresholding the set of coefficients to keep only largest amplitude coefficients.

```fwT = keep_biggest(fw, round(n*.08));
options.part = part;
f1 = perform_alpert_transform_nd(fwT,pos,k, -1,options);
```

At last we can display the approximated function. Since the original function is smooth, the approximation produces a small error.

```clf;
subplot(1,2,1);
plot_spherical_function(pos,f);
title('Original signal');
subplot(1,2,2);
plot_spherical_function(pos,f1);
title('Approximated signal');
``` ## Other features of the toolbox

The toolbox implements several other features, among which:

• Functions build_alpert_matrix and build_alpert_matrix_2d to build the transformation matrix, but these are quite slow.
• Sliced 2D transforms

A sliced tranform is implemented in function perform_alpert_transform_2d_sliced. It divides the points into s slices and perform a 2D transform on each slice. You have to provide a number of slices, which fixes the resolution on the Y axis (as if you were decomposing the function on a wavelet basis for the X direction, and on a box spline basis for the Y direction). This is not very usefull unless you want to set a given resolution on the X axis.