# GSP Graph TV Demo - Reconstruction of missing sample on a graph using TV¶

## Description¶

Reconstruction of missing sample on a graph using TV

In this demo, we try to reconstruct missing sample of a piece-wise smooth signal on a graph. To do so, we will minimize the well-known TV norm defined on the graph.

For this example, you need the pyunlocbox. You can download it from https://github.com/epfl-lts2/pyunlocbox and installing it.

We express the recovery problem as a convex optimization problem of the following form:

Where \(b\) represents the known measurements, \(M\) is an operator representing the mask and \(\epsilon\) is the radius of the l2 ball.

We set:

- \(f_1(x)=||\nabla x ||_1\)

We define the prox of \(f_1\) as:

- \(f_2\) is the indicator function of the set S define by :math:||Mx-b||_2 < epsilon

We define the prox of \(f_2\) as

with \(i_S(x)\) is zero if \(x\) is in the set \(S\) and infinity otherwise. This previous problem has an identical solution as:

It is simply a projection on the B2-ball.

## Results and code¶

```
>>> from pygsp import graphs, plotting
>>> import numpy as np
>>>
>>> # Create a random sensor graph
>>> G = graphs.Sensor(N=256, distribute=True)
>>> G.compute_fourier_basis()
>>>
>>> # Create signal
>>> graph_value = np.copysign(np.ones(np.shape(G.U[:, 3])[0]), G.U[:, 3])
>>>
>>> plotting.plt_plot_signal(G, graph_value, savefig=True, plot_name='doc/tutorials/img/original_signal')
```

This figure shows the original signal on graph.

```
>>> # Create the mask
>>> M = np.random.rand(G.U.shape[0], 1)
>>> M = M > 0.6 # Probability of having no label on a vertex.
>>>
>>> # Applying the mask to the data
>>> sigma = 0.0
>>> depleted_graph_value = M * (graph_value.reshape(graph_value.size, 1) + sigma * np.random.standard_normal((G.N, 1)))
>>>
>>> plotting.plt_plot_signal(G, depleted_graph_value, show_edges=True, savefig=True, plot_name='doc/tutorials/img/depleted_signal')
```

This figure shows the signal on graph after the application of the mask and addition of noise. More than half of the vertices are set to 0.

This figure shows the reconstructed signal thanks to the algorithm.

## Comparison with Tikhonov regularization¶

We can also use the Tikhonov regularizer that will promote smoothness. In this case, we solve:

The result is presented as following:

This figure shows the reconstructed signal thanks to the algorithm.