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

In [1]:
using NtToolBox
using PyPlot
WARNING: Method definition macroexpand(Module, Any) in module Compat at C:\Users\Ayman\.julia\v0.5\Compat\src\Compat.jl:1491 overwritten in module MacroTools at C:\Users\Ayman\.julia\v0.5\MacroTools\src\utils.jl:64.

Image Loading and Displaying

Several functions are implemented to load and display images.

First we load an image.

path to the images

In [2]:
name = "NtToolBox/src/data/lena.png"
n = 256
M = load_image(name, n);

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

In [3]:
imageplot(M[Int(n/2 - 25) : Int(n/2 + 25), Int(n/2 - 25) : Int(n/2 + 25)], "Zoom", [1, 2, 2])
PyObject <matplotlib.text.Text object at 0x000000001ACF73C8>

Image Modification

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

In [4]:
imageplot(-M, "-M", [1,2,1])
imageplot(M[end:-1:1,1:size(M, 2)], "Flipped", [1,2,2])
PyObject <matplotlib.text.Text object at 0x000000001BED59E8>

Blurring is achieved by computing a convolution with a kernel.

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

In [5]:
sigma = 5
X = [0:n/2; -n/2:-2]'
Y = [0:n/2; -n/2:-2]
h = exp(-(X.^2 .+ Y.^2)/(2*(sigma)^2))
h = h/sum(h)

Compute the periodic convolution ussing FFTs

In [6]:
Mh = conv2(Array{Float64, 2}(M), h)
Mh = Mh[1:255, 1:255] + Mh[257:511, 1:255] + Mh[1:255, 257:511] + Mh[257:511, 257:511];


In [7]:
imageplot(M, "Image", [1, 2, 1])
imageplot(Mh, "Blurred", [1, 2, 2])
PyObject <matplotlib.text.Text object at 0x000000001C3D5320>

Several differential and convolution operators are implemented.

In [8]:
(G_x, G_y) = Images.imgradients(M)
imageplot(G_x, "d/ dx", [1, 2, 1])
imageplot(G_y, "d/ dy", [1, 2, 2])
WARNING: the order of outputs has switched (`grad1, grad2 = imgradients(img)` rather than `gradx, grady = imgradients`). Silence this warning by providing a kernelfun, e.g., imgradients(img, KernelFactors.ando3).
 in depwarn(::String, ::Symbol) at .\deprecated.jl:64
 in imgradients(::Array{Float32,2}) at C:\Users\Ayman\.julia\v0.5\ImageFiltering\src\specialty.jl:50
 in include_string(::String, ::String) at .\loading.jl:441
 in execute_request(::ZMQ.Socket, ::IJulia.Msg) at C:\Users\Ayman\.julia\v0.5\IJulia\src\execute_request.jl:157
 in eventloop(::ZMQ.Socket) at C:\Users\Ayman\.julia\v0.5\IJulia\src\eventloop.jl:8
(::IJulia.##13#19)() at .\task.jl:360
while loading In[8], in expression starting on line 1
PyObject <matplotlib.text.Text object at 0x000000001DBAAEF0>

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 $(n/2+1,n/2+1)$.

In [9]:
Mf = plan_fft(M)
Lf = fftshift(log(abs(Mf*M) + 1e-1))
imageplot(M, "Image", [1, 2, 1])
imageplot(Lf, "Fourier transform", [1, 2, 2])
PyObject <matplotlib.text.Text object at 0x000000001DC00208>

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 [10]:
PyObject <matplotlib.text.Text object at 0x000000001DE049B0>

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

In [11]:
PyObject <matplotlib.text.Text object at 0x000000001E0E8CC0>
In [ ]: