Dictionary Learning for Denoising¶

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This tour follows the Dictionary Learning numerical tour. It uses a learned dictionary to perform image denoising.

Learning the Dictionary¶

We aim at applying the dictionary learning method to denoising. We thus consider a noisy image obtained by adding Gaussian noise to a clean, unknown image $f_0 \in \RR^N$ where $N=n_0 \times n_0$.

Noise level $\sigma$.

Size of the image.

Load the image.

Display $f_0$.

Noisy observations $f=f_0+w$ where $w$ is a realization of $\Nn(0,\si^2\text{Id}_N)$.

Display $f$.

Perform the numerical tour on Dictionary Learning to obtain a dictionary $D \in \RR^{n \times p}$

Width $w$ of the patches.

Number $p$ of atoms.

Dimension $n= w \times w$ of the data to be sparse coded.

Denoising by Sparse Coding¶

Here we apply sparse coding in the learned dictionary to the problem of image denoising, as detailed in:

M. Elad and M. Aharon, <http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4011956 Image Denoising Via Sparse and Redundant representations over Learned Dictionaries>, IEEE Trans. on Image Processing, Vol. 15, no. 12, pp. 3736-3745, December 2006.

The method first extract lots of patches from $f$, then perform sparse coding of each patch in $D$, and then average the overlapping patch to obtained the denoised image $f_1$.

We extract a large number $m$ of patches $Y = (y_j)_{j=1}^m \in \RR^{n \times m}$ from the noisy image $f$.

To extract the patche, we use $$ y_j(t) = f( a_j + t ) $$ where $a_j$ is the location of patch indexed by $j$.

In practice, splitting $j=(j_1,j_2)$ as a 2-D index, we use $a_j = (j_1 q,j_2 q) $ where $q>0$ is an overlap factor ( so that setting $q=w$ implies no overlap). Overlap is important to obtain good denoising performance (reduced blocking artifact).

Define regularly space positions for the extraction of patches.

Ensure boundary conditions (reflexion).

Extract the $m$ patches $Y$.

Save the mean $\theta_j$ of each patch appart, and remove it.

Denoising of the patches is obtained by performing a sparse coding of each patch $y_j$ in $D$ $$ \umin{\norm{D x_j - y_j} \leq \epsilon} \norm{x_j}_1. $$

The value of $\epsilon>0$ is set proportionaly to the noise level $\sqrt{n}\sigma$ that contaminates each patch. We denote $\rho \approx 1$ the proportionality factor, that is a parameter of the method.

The sparse coding problem can written equivalently in a way that is easier to deal using proximal splitting schemes. We introduce an auxiliary variable $u=D x \in \RR^n$ as follow $$ \umin{ z=(x,u) \in \RR^p \times \RR^n } F(z) + G(z) $$ where for $z=(x,u)$, one defines $$ F(x,u) = \norm{x}_1 + \iota_{B_\epsilon(y)}(u) \qwhereq B_\epsilon(y) = \enscond{u}{ \norm{u-y} \leq \epsilon } $$ and $$ G(x,u) = \iota_{\Cc}(x,u) \qwhereq \Cc = \enscond{(x,u)}{ u=D x }. $$

Here we included the constraints using characteristic functions $$ \iota_{A}(z) = \choice{ 0 \qifq z \in A, \\ +\infty \quad \text{otherwise}. } $$

Douglas-Rachford Algorithm¶

To minimize the sparse coding problem, we make use of a proximal splitting scheme to minimize an energy of the form $F(z)+G(z)$. These schemes are adapted to solve structured non-smooth optimization problem.

They basically replace the traditional gradient-descent step (that is not available because neither $F$ nor $G$ are smooth functionals) by proximal mappings, defined as $$ \text{Prox}_{\gamma F}(z) = \uargmin{y} \frac{1}{2}\norm{z-y}^2 + \ga F(y) $$ (the same definition applies also for $G$).

The Douglas-Rachford (DR) algorithm is an iterative scheme to minimize functionals of the form $$ \umin{z} F(z) + G(z) $$ where $F$ and $G$ are convex functions for which one is able to comptue the proximal mappings $ \text{Prox}_{\gamma F} $ and $ \text{Prox}_{\gamma G} $.

The important point is that $F$ and $G$ do not need to be smooth. One onely needs then to be "proximable".

A DR iteration reads $$ \tilde z_{k+1} = \pa{1-\frac{\mu}{2}} \tilde z_k + \frac{\mu}{2} \text{rPox}_{\gamma G}( \text{rProx}_{\gamma F}(\tilde z_k) ) \qandq z_{k+1} = \text{Prox}_{\gamma F}(\tilde z_{k+1},) $$

We have use the following shortcuts: $$ \text{rProx}_{\gamma F}(z) = 2\text{Prox}_{\gamma F}(z)-z $$

It is of course possible to inter-change the roles of $F$ and $G$, which defines another set of iterations.

One can show that for any value of $\gamma>0$, any $ 0 < \mu < 2 $, and any $\tilde z_0$, $z_k \rightarrow z^\star$ which is a minimizer of $F+G$.

Please note that it is actually $z_k$ that converges, and not $\tilde z_k$.

To learn more about this algorithm, you can read:

Proximal Splitting Methods in Signal Processing, Patrick L. Combettes and Jean-Christophe Pesquet, in: Fixed-Point Algorithms for Inverse Problems in Science and Engineering, New York: Springer-Verlag, 2010.

Douglas-Rachford for Sparse Coding¶

In the special case of the constrained sparse coding problem, the proximal mapping of $G$ is the orthogonal projection on the convex set $\Cc$: $$ (\tilde x, \tilde u) = \text{Prox}_{\ga G}(x,u) = \text{Proj}_\Cc(x,u). $$

It can be computed by solving a linear system of equations since $$ \tilde u = D \tilde x \qwhereq \tilde x = (\text{Id} + D^* D)^{-1}(f + D^* u). $$

Define Proj$_\Cc$, by pre-compuring the inverse of $\text{Id} + D^* D$.

One has $\text{Prox}_{\ga G} = \text{Proj}_{\Cc}$, whatever the value of $\ga$.

Function $F(x,u)$ is actully a separable sum of a function that only depends on $x$ and a function that depends only on $u$: $$ F(x,u) = \iota_{B_\epsilon(y)}(u) + \norm{x}_1. $$ The proximal operator of $F$ reads $$ \text{Prox}_{\ga F}(x,u) = ( \text{Proj}_{B_\epsilon(y)}(u), \text{Prox}_{\ga \norm{\cdot}_1 }(x) ). $$

In order to speed up the implementation, the DR algorithm will be performed in parallel on all the $x_j$. We thus define $y=Y$ to be the set of all the patches.

Define the projector $$ \text{Proj}_{B_\epsilon(y)}(u) = y + (u-y) \max\pa{1 , \frac{\epsilon}{\norm{u-y}} }$ $$

The proximal operator of the $ \ell^1 $ norm $\norm{\cdot}_1$ is a soft thresholding: $$ \text{Prox}_{\ga \norm{\cdot}_1}(x)_i = \max\pa{ 0, \frac{\ga}{\abs{x_i}} } x_i. $$

Define the proximal operator of $F$.

Set the value of $\mu$ and $\gamma$. You might consider using your own value to speed up the convergence.

Number of iterations.

Exercise 1

Implement the DR iterative algorithm on |niter| iterations. Keep track of the evolution of the minimized energy $ \norm{x}_1 $ during the iterations.

Patch Averaging¶

Once each $x_j$ is computed, one obtains the denoised patch $\tilde y_j = D x_j$. These denoised patches are then aggregated together to obtained the denoised image: $$ f_1(t) = \frac{1}{W_t} \sum_j \tilde y_j(t-a_j) $$ where $W_t$ is the number of patches that overlap at a given pixel location $t$ (note that in this formula, we assumed $\tilde y_j(t-a_j)=0$ when $t-a_j$ falls outside the patch limits).

Approximated patches $ Y_1 = (\tilde y_j)_j = D X$.

Insert back the mean.

To obtain the denoising, we average the value of the approximated patches $ Y_1 $ that overlap.

Display the result.