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Spherical Haar Wavelets

Spherical Haar Wavelets

This tour explores multiscale computation on a 3D multiresolution sphere using a face-based haar transform.


Installing toolboxes and setting up the path.

You need to download the following files: signal toolbox, general toolbox, graph toolbox and wavelet_meshes toolbox.

You need to unzip these toolboxes in your working directory, so that you have toolbox_signal, toolbox_general, toolbox_graph and toolbox_wavelet_meshes in your directory.

For Scilab user: you must replace the Matlab comment '%' by its Scilab counterpart '//'.

Recommandation: You should create a text file named for instance numericaltour.sce (in Scilab) or numericaltour.m (in Matlab) to write all the Scilab/Matlab command you want to execute. Then, simply run exec('numericaltour.sce'); (in Scilab) or numericaltour; (in Matlab) to run the commands.

Execute this line only if you are using Matlab.

getd = @(p)path(p,path); % scilab users must *not* execute this

Then you can add the toolboxes to the path.


Functions Defined on Surfaces

One can define a function on a discrete 3D mesh that assigns a value to each vertex. One can then perform processing of the function according to the geometry of the surface. Here we use a simple sphere.

First compute a multiresolution sphere.

options.base_mesh = 'ico';
options.relaxation = 1;
options.keep_subdivision = 1;
J = 7;
[vertex,face] = compute_semiregular_sphere(J,options);
n = size(face{end},2);

Display two examples of sphere.

for j=[1 2 3 4]
    subplot(2, 2, j);
    plot_mesh(vertex{j}, face{j});
    shading faceted;

Comput the center of each face.

x = [];
for i=1:3
    v = vertex{end}(i,:);
    x(i,:) = mean(v(face{end}));

Load an image.

name = 'lena';
M = rescale( load_image(name, 512) );

Display it.


Load a function on the sphere. Use the center of each face to sample the function.

f = rescale( load_spherical_function(M, x, options) );

Display the function on the sphere.

vv = [125,-15];
options.face_vertex_color = f;
plot_mesh(vertex{end}, face{end}, options);
colormap gray(256);
lighting none;

Multiscale Low Pass

One can compute low pass approximation by iteratively averaging over 4 neighboring triangles.

Perform one low pass filtering.

f1 = mean( reshape(f, [length(f)/4 4]), 2);


options.face_vertex_color = f1;
plot_mesh(vertex{end-1}, face{end-1}, options);
lighting none;

Exercice 1: (check the solution) Compute the successive low pass approximations.


Spherical Haar Transform

One can compute a wavelet transform by extracting, at each scale, 3 orthogonal wavelet coefficient to represent the orthogonal complement between the successive resolutions.

Precompute the local wavelet matrix, which contains the local vector and three orthognal detail directions.

U = randn(4);
U(:,1) = 1;
[U,R] = qr(U);

Initialize the forward transform.

fw = f;
nj = length(f);

Extract the low pass component and apply the matrix U

fj = fw(1:nj);
fj = reshape(fj, [nj/4 4]);
fj = fj*U;

Store back the coefficients.

fw(1:nj) = fj(:);
nj = nj/4;

Exercice 2: (check the solution) Compute the full wavelet transform, and check for orthogonality (conservation of energy).

Orthogonality deviation (should be 0): 5.5081e-15

Display the coefficients "in place".

options.face_vertex_color = clamp(fw,-2,2);
plot_mesh(vertex{end}, face{end}, options);
colormap gray(256);
lighting none;

Display the decay of the coefficients.

axis([1 n -5 5]);

Exercice 3: (check the solution) Implement the backward spherical Haar transform (replace U by U' to perform the reconstruction), and check for perfect reconstruction.


options.face_vertex_color = clamp(f1);
plot_mesh(vertex{end}, face{end}, options);
colormap gray(256);
lighting none;
Bijectivity deviation (should be 0): 4.0915e-16

Exercice 4: (check the solution) Perform Haar wavelet approximation with only 10% of the coefficients.


Exercice 5: (check the solution) Compare with the traditional 2D Haar approximation of M.


Exercice 6: (check the solution) Implement Spherical denoising using the Haar transform. Compare it with vertex-based lifting scheme denoising.