Image Approximation with Fourier and Wavelets¶

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 overviews the use of Fourier and wavelets for image approximation.

In [1]:

from __future__ import division

import numpy as np
import scipy as scp
import pylab as pyl
import matplotlib.pyplot as plt

from nt_toolbox.general import *
from nt_toolbox.signal import *

import warnings
warnings.filterwarnings('ignore')

%matplotlib inline
%load_ext autoreload
%autoreload 2

Note: to measure the error of an image $f$ with its approximation $f_M$, we use the SNR measure, defined as

$$ \text{SNR}(f,f_M) = -20\log_{10} \pa{ \frac{ \norm{f-f_M} }{ \norm{f} } }, $$

which is a quantity expressed in decibels (dB). The higer the SNR, the better the quality.

Image Loading and Displaying¶

First we load an image $ f \in \RR^N $ of $ N = N_0 \times N_0 $ pixels.

In [2]:

n0 = 512
f = rescale(load_image("nt_toolbox/data/hibiscus.bmp", n0))

Display the original image.

In [3]:

plt.figure(figsize = (5,5))
imageplot(f, 'Image f')

Display a zoom in the middle.

In [4]:

plt.figure(figsize = (5,5))
imageplot(f[n0//2 - 32:n0//2 + 32,n0//2 - 32:n0//2 + 32], 'Zoom')

An image is a 2D array, it can be modified as a matrix.

In [5]:

plt.figure(figsize = (8,8))
imageplot(-f, '-f', [1, 2, 1])
imageplot(f[::-1,], 'Flipped', [1, 2, 2])

Blurring is achieved by computing a convolution $f \star h$ with a kernel $h$.

Compute the low pass kernel.

In [6]:

k = 9; #size of the kernel
h = np.ones([k,k])
h = h/np.sum(h) #normalize

Compute the convolution $f \star h$.

In [7]:

from scipy import signal
fh = signal.convolve2d(f, h, boundary = "symm")

Display.

In [8]:

plt.figure(figsize = (5,5))
imageplot(fh, 'Blurred image')

Fourier Transform¶

The Fourier orthonormal basis is defined as $$ \psi_m(k) = \frac{1}{\sqrt{N}}e^{\frac{2i\pi}{N_0} \dotp{m}{k} } $$ where $0 \leq k_1,k_2 < N_0$ are position indexes, and $0 \leq m_1,m_2 < N_0$ are frequency indexes.

The Fourier transform $\hat f$ is the projection of the image on this Fourier basis

$$ \hat f(m) = \dotp{f}{\psi_m}. $$

The Fourier transform is computed in $ O(N \log(N)) $ operation using the FFT algorithm (Fast Fourier Transform). Note the normalization by $\sqrt{N}=N_0$ to make the transform orthonormal.

In [9]:

F = pyl.fft2(f)/n0

We check this conservation of the energy.

In [10]:

from pylab import linalg

print("Energy of Image:   %f" %linalg.norm(f))
print("Energy of Fourier: %f" %linalg.norm(F))

Energy of Image:   205.747421
Energy of Fourier: 205.747421

Compute the logarithm of the Fourier magnitude $ \log\left(\abs{\hat f(m)} + \epsilon\right) $, for some small $\epsilon$.

In [11]:

L = pyl.fftshift(np.log(abs(F) + 1e-1))

Display. Note that we use the function fftshift to put the 0 low frequency in the middle.

In [12]:

plt.figure(figsize = (5,5))
imageplot(L, 'Log(Fourier transform)')

Linear Fourier Approximation¶

An approximation is obtained by retaining a certain set of index $I_M$

$$ f_M = \sum_{ m \in I_M } \dotp{f}{\psi_m} \psi_m. $$

Linear approximation is obtained by retaining a fixed set $I_M$ of $M = \abs{I_M}$ coefficients. The important point is that $I_M$ does not depend on the image $f$ to be approximated.

For the Fourier transform, a low pass linear approximation is obtained by keeping only the frequencies within a square.

$$ I_M = \enscond{m=(m_1,m_2)}{ -q/2 \leq m_1,m_2 < q/2 } $$

where $ q = \sqrt{M} $.

This can be achieved by computing the Fourier transform, setting to zero the $N-M$ coefficients outside the square $I_M$ and then inverting the Fourier transform.

Number $M$ of kept coefficients.

In [13]:

M = n0**2//64

Exercise 1

Perform the linear Fourier approximation with $M$ coefficients. Store the result in the variable $f_M$.

In [14]:

run -i nt_solutions/introduction_4_fourier_wavelets/exo1