Wavelet Block Thresholding¶

Important: Please read the installation page for details about how to install the toolboxes. $\newcommand{\dotp}[2]{\langle #1, #2 \rangle}$ $\newcommand{\enscond}[2]{\lbrace #1, #2 \rbrace}$ $\newcommand{\pd}[2]{ \frac{ \partial #1}{\partial #2} }$ $\newcommand{\umin}[1]{\underset{#1}{\min}\;}$ $\newcommand{\umax}[1]{\underset{#1}{\max}\;}$ $\newcommand{\umin}[1]{\underset{#1}{\min}\;}$ $\newcommand{\uargmin}[1]{\underset{#1}{argmin}\;}$ $\newcommand{\norm}[1]{\|#1\|}$ $\newcommand{\abs}[1]{\left|#1\right|}$ $\newcommand{\choice}[1]{ \left\{ \begin{array}{l} #1 \end{array} \right. }$ $\newcommand{\pa}[1]{\left(#1\right)}$ $\newcommand{\diag}[1]{{diag}\left( #1 \right)}$ $\newcommand{\qandq}{\quad\text{and}\quad}$ $\newcommand{\qwhereq}{\quad\text{where}\quad}$ $\newcommand{\qifq}{ \quad \text{if} \quad }$ $\newcommand{\qarrq}{ \quad \Longrightarrow \quad }$ $\newcommand{\ZZ}{\mathbb{Z}}$ $\newcommand{\CC}{\mathbb{C}}$ $\newcommand{\RR}{\mathbb{R}}$ $\newcommand{\EE}{\mathbb{E}}$ $\newcommand{\Zz}{\mathcal{Z}}$ $\newcommand{\Ww}{\mathcal{W}}$ $\newcommand{\Vv}{\mathcal{V}}$ $\newcommand{\Nn}{\mathcal{N}}$ $\newcommand{\NN}{\mathcal{N}}$ $\newcommand{\Hh}{\mathcal{H}}$ $\newcommand{\Bb}{\mathcal{B}}$ $\newcommand{\Ee}{\mathcal{E}}$ $\newcommand{\Cc}{\mathcal{C}}$ $\newcommand{\Gg}{\mathcal{G}}$ $\newcommand{\Ss}{\mathcal{S}}$ $\newcommand{\Pp}{\mathcal{P}}$ $\newcommand{\Ff}{\mathcal{F}}$ $\newcommand{\Xx}{\mathcal{X}}$ $\newcommand{\Mm}{\mathcal{M}}$ $\newcommand{\Ii}{\mathcal{I}}$ $\newcommand{\Dd}{\mathcal{D}}$ $\newcommand{\Ll}{\mathcal{L}}$ $\newcommand{\Tt}{\mathcal{T}}$ $\newcommand{\si}{\sigma}$ $\newcommand{\al}{\alpha}$ $\newcommand{\la}{\lambda}$ $\newcommand{\ga}{\gamma}$ $\newcommand{\Ga}{\Gamma}$ $\newcommand{\La}{\Lambda}$ $\newcommand{\si}{\sigma}$ $\newcommand{\Si}{\Sigma}$ $\newcommand{\be}{\beta}$ $\newcommand{\de}{\delta}$ $\newcommand{\De}{\Delta}$ $\newcommand{\phi}{\varphi}$ $\newcommand{\th}{\theta}$ $\newcommand{\om}{\omega}$ $\newcommand{\Om}{\Omega}$

This numerical tour presents block thresholding methods, that makes use of the structure of wavelet coefficients of natural images to perform denoising. Theoretical properties of block thresholding were investigated in CaiSilv Cai99 HallKerkPic99

In [2]:

using PyPlot
using NtToolBox
# using Autoreload
# arequire("NtToolBox")

Generating a Noisy Image¶

Here we use an additive Gaussian noise.

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

In [3]:

n = 256;

First we load an image $f_0 \in \RR^N$.

In [3]:

f0 = rescale(load_image("NtToolBox/src/data/boat.png", n));

Display it.

In [4]:

figure(figsize = (5,5))
imageplot(f0)

Noise level.

In [5]:

sigma = .08;

Generate a noisy image $f=f_0+\epsilon$ where $\epsilon \sim \Nn(0,\si^2\text{Id}_N)$.

In [6]:

using Distributions
f = f0 + sigma.*rand(Normal(), n, n);

Display it.

In [7]:

figure(figsize = (5,5))
imageplot(clamP(f))

Orthogonal Wavelet Thresholding¶

We first consider the traditional wavelet thresholding method.

Parameters for the orthogonal wavelet transform.

In [8]:

Jmin = 4;

Shortcuts for the foward and backward wavelet transforms.

In [9]:

wav  = f -> NtToolBox.perform_wavelet_transf(f, Jmin, +1)
iwav = fw -> NtToolBox.perform_wavelet_transf(fw, Jmin, -1);

Display the original set of noisy coefficients.

In [10]:

figure(figsize = (10, 10))
plot_wavelet(wav(f), Jmin)
show()