Linear Image Denoising¶

This numerical tour introduces basic image denoising methods.

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Noisy Image Formation¶

In these numerical tour, we simulate noisy acquisition by adding some white noise (each pixel is corrupted by adding an independant Gaussian variable).

This is useful to test in an oracle maner the performance of our methods.

Size $N = n \times n$ of the image.

We load a clean image $x_0 \in \RR^N$.

Display the clean image.

Variance of the noise.

We add some noise to it to obtain the noisy signal $y = x_0 + w$. Here $w$ is a realization of a Gaussian white noise of variance $\si^2$.

Display the noisy image.

Linear Image Denoising¶

We consider a noising estimator $x \in \RR^N$ of $x_0$ that only depends on the observation $y$. Mathematically speaking, it is thus a random vector that depends on the noise $w$.

A translation invariant linear denoising is necessarely a convolution with a kernel $h$ $$ x = x_0 \star h $$ where the periodic convolution between two 2-D arrays is defined as $$ (a \star b)_i = \sum_j a(j) b(i-j). $$

It can be computed over the Fourier domain as $$ \forall \om, \quad \hat x(\om) = \hat x_0(\om) \hat h(\om). $$

We use here a Gaussian fitler $h$ parameterized by the bandwith $\mu$.

Display the filter $h$ and its Fourier transform.

Shortcut for the convolution with $h$.

Display a denoised signal.

Exercise 1: Display a denoised signal for several values of $\mu$.