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# Introduction to Image Processing

This numerical tour explores some basic image processing tasks.

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

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from nt_toolbox.general import *
from nt_toolbox.signal import *
%pylab inline
%matplotlib inline
Populating the interactive namespace from numpy and matplotlib
/Users/gpeyre/anaconda/envs/p34/lib/python3.4/site-packages/IPython/core/magics/ UserWarning: pylab import has clobbered these variables: ['pylab']
`%matplotlib` prevents importing * from pylab and numpy
  "\n`%matplotlib` prevents importing * from pylab and numpy"

Image Loading and Displaying

Several functions are implemented to load and display images.

First we load an image.

path to the images

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name = 'nt_toolbox/data/hibiscus.bmp'
n = 256
M = load_image(name, n)
/Users/gpeyre/anaconda/envs/p34/lib/python3.4/site-packages/skimage/transform/ UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.
  warn("The default mode, 'constant', will be changed to 'reflect' in "

We can display it. It is possible to zoom on it, extract pixels, etc.

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m = int(n/2)
imageplot(M[m-25:m+25,m-25:m+25], 'Zoom', [1, 2, 2])

Image Modification

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

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imageplot(-M, '-M', [1,2,1])
imageplot(M[::-1,:], 'Flipped', [1,2,2])

Blurring is achieved by computing a convolution with a kernel.

Compute the low pass Gaussian kernel. Warning, the indexes needs to be modulo $n$ in order to use FFTs.

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sigma = 5
t = concatenate( (arange(0,n/2+1), arange(-n/2,-1)) )
[Y,X] = np.meshgrid(t,t)
h = exp( -(X**2+Y**2)/(2.0*float(sigma)**2) )
h = h/sum(h)

Compute the periodic convolution ussing FFTs

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Mh = real( ifft2(fft2(M) * fft2(h)) )


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imageplot(M, 'Image', [1, 2, 1])
imageplot(Mh, 'Blurred', [1, 2, 2])

Several differential and convolution operators are implemented.

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G = grad(M)
imageplot(G[:,:,0], 'd/ dx', [1, 2, 1])
imageplot(G[:,:,1], 'd/ dy', [1, 2, 2])

Fourier Transform

The 2D Fourier transform can be used to perform low pass approximation and interpolation (by zero padding).

Compute and display the Fourier transform (display over a log scale). The function fftshift is useful to put the 0 low frequency in the middle. After fftshift, the zero frequency is located at position $(n/2+1,n/2+1)$.

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Mf = fft2(M)
Lf = fftshift(log(abs(Mf) + 1e-1))
imageplot(M, 'Image', [1, 2, 1])
imageplot(Lf, 'Fourier transform', [1, 2, 2])

Exercise 1: To avoid boundary artifacts and estimate really the frequency content of the image (and not of the artifacts!), one needs to multiply M by a smooth windowing function h and compute fft2(M*h). Use a sine windowing function. Can you interpret the resulting filter ?

In [26]:
run -i nt_solutions/introduction_3_image/exo1

Exercise 2: Perform low pass filtering by removing the high frequencies of the spectrum. What do you oberve ?

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run -i nt_solutions/introduction_3_image/exo2