Inpainting using Variational Regularization

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This numerical tour explores the use of variational energies (Sobolev, total variation) to regularize the image inpaiting problem.

Here we consider inpainting of damaged observation without noise.

In [2]:
addpath('toolbox_signal')
addpath('toolbox_general')
addpath('solutions/inverse_4_inpainting_variational')

Missing Pixels and Inpainting

Inpainting corresponds to filling holes in images.

First we load the image $f_0 \in \RR^N$ of $N=n\times n$ to be inpainted.

In [3]:
name = 'cameraman';
n = 256;
f0 = rescale( load_image(name, n) );

Display the original image.

In [4]:
clf;
imageplot(f0);

Ratio of removed pixels.

In [5]:
rho = .7;

Construct a random mask $\Ga = \chi_{\Om}$ so that $\Ga_i=0$ for removed pixels $i \notin \Om$, and $\Ga_i=1$ for kept pixels $i \in \Om$.

In [6]:
Gamma = rand(n)>rho;

We create the masking operator $\Phi$ which is a diagonal operator: $$ (\Phi f)_i = \Ga_i f_i $$

In [7]:
Phi = @(f)f.*Gamma;

Compute the damaged observation $y=\Phi(f_0)$ (no noise is added).

In [8]:
y = Phi(f0);

Display the observations.

In [9]:
clf;
imageplot(y);

Sobolev Impainting

We solve the inpainting problem by minimzing the Sobolev norm of the image under the constraint of matching the observation $$ f^\star = \uargmin{ \Phi(f) = y } E(f) = \norm{\nabla f}^2 $$ where $\nabla$ is a finite difference approximation of the gradient.

It can be shown that the solution to this problem is an harmonic function with prescribed boundary condition $$ \forall i \notin \Om, \quad (\Delta f^\star)_i=0 \qandq \forall i \in \Om, \quad f^\star_i = y_i. $$

This problem requires the constrained minimization of a smooth function, it can thus be solved using a projected gradient descent $$ f^{(\ell+1)} = \Pi \pa{ f^{(\ell)} + \tau \Delta(f^{(\ell)}) }$$ where $ \Pi $ is the orthogonal projector on the constraint $y=\Phi f$ $$ (\Pi f)_i = \choice{ y_i \qifq i \in \Om, \\ f_i \qifq i \notin \Om, \\ }$ $$

In [10]:
Pi = @(f)f.*(1-Gamma) + y.*Gamma;

Here $ \Delta = -\nabla^* \circ \nabla = \text{div} \circ \nabla $ is the gradient of the Sobolev energy $ E $.

In [11]:
Delta = @(f)div(grad(f));

For convergence, the gradient descent step size should satisfy: $$ \tau<\frac{2}{\norm{\Delta}}=\frac{1}{4} $$

In [12]:
tau = .8/4;

Exercise 1

Perform the projected gradient descent. Record in a variable |E| the evolution of the Sobolev energy $E$.

In [13]:
exo1()
In [14]:
%% Insert your code here.

Display the decay of the energy $E(f^{(\ell)})$ with the iterations.

In [15]:
clf;
plot(E); axis('tight');
set_label('Iteration #', 'E');

Display the result.

In [16]:
clf;
imageplot(f, strcat(['Inpainted, SNR=' num2str(snr(f0,f),3) 'dB']));

Inpainting with TV Regularization

A non-linear prior replaces the Sobolev energy by the TV norm, that tends to better reconstruct edges. Here we use a smoothed TV norm to avoid convergence issue with gradient descent algorithms.

The smoothed TV norm reads: $$ J_\epsilon(f) = \sum_x \sqrt{\norm{ \nabla f(x) }^2+\epsilon^2} $$

We use a projected gradient descent to solve this problem $$ f^{(\ell+1)} = \Pi \pa{ f^{(\ell)} + \tau G_\epsilon(f^{(\ell)}) }$$ where $ G_\epsilon $ is the gradient of $J_\epsilon$, that is defined as $$ G_\epsilon(f) = -\text{div} N_\epsilon( \nabla f ) $$ where $ N_\epsilon $ is the following normalization operator $$ N_\epsilon(u)_i = \frac{u_i}{ \sqrt{\norm{u_i}^2 + \epsilon^2} } $$ that is applied to any vector field $u=(u_i)_i \in \RR^{N \times 2} $ for $u_i \in \RR^2$.

Regularization parameter $\epsilon$ for the TV norm

In [17]:
epsilon = 1e-2;

Define the normalization operator.

In [18]:
Amplitude = @(u)sqrt(sum(u.^2,3)+epsilon^2);
Neps = @(u)u./repmat(Amplitude(u), [1 1 2]);

The step size $\tau$, should satisfy $$ \tau<\frac{\epsilon}{4}. $$

In [19]:
tau = .9*epsilon/4;

Define the gradient of $J$

In [20]:
G = @(f)-div(Neps(grad(f)));

Exercise 2

Perform the projected gradient descent. Record in a variable |J| the evolution of the TV energy $J_\epsilon$.

In [21]:
exo2()
In [22]:
%% Insert your code here.

Display the result.

In [23]:
clf;
imageplot(clamp(f), strcat(['SNR=' num2str(snr(f0,f),3) 'dB']));

Display the evolution of the TV norm $J_\epsilon$.

In [24]:
clf;
plot(J); 
axis('tight');
set_label('Iteration #', 'J_\epsilon');

Inpainting with non-random mask

Inpainting can be used to remove objects in pictures.

Load an image.

In [25]:
n = 256;
f0 = load_image('parrot', n);
f0 = rescale( sum(f0,3) );

Display it.

In [26]:
clf;
imageplot(f0);

Load the mask.

In [27]:
Gamma = load_image('parrot-mask', n);
Gamma = double(rescale(Gamma)>.5);

Masking operator $\Phi$.

In [28]:
Phi = @(f)f.*Gamma;

Observation $y=\Phi(f_0)$.

In [29]:
y = Phi(f0);

Display it.

In [30]:
clf;
imageplot(y);