COMP 4500 SQU Computer Vision and Image Processing Project
Sohar UniversityFaculty of Engineering
Electrical & Computer Engineering Program
Lab Exercise
Computer Vision and Image Processing
COMP4500
2
Course Coordinator:
Assis. Prof. Dr. Anas Quteishat
E-mail : aquteishat@soharuni.edu.om
Office
: G-3-15
Tel
: 26720101 EXT 236
Year: 2021-2022
FACULTY OF ENGINEERING – SOHAR
UNIVERSITY
COMP4500
Module 2 Lab Exercise
Introduction to MATLAB Digital Image Processing
1. Lab Objectives:
Performing image enhancement using different type of
intensity
transformations.
Obtaining the histogram of an image.
2. Theory
The first part of this experiment will deal with enhancement. The principal objective
of enhancement is to process an image so that the result is more suitable than the
original image for a specific application. The enhancement approaches utilized in
this experiment are used for the sake of general-purpose contrast manipulation.
These approaches are referred to as point processing (because they manipulate
still-pixel images).
Image enhancement is a very basic image processing task that defines us to have
a better subjective judgment over the images. And Image Enhancement in spatial
domain (that is, performing operations directly on pixel values) is the very
simplistic approach. Enhanced images provide better contrast of the details that
images contain. Image enhancement is applied in every field where images are
ought to be understood and analyzed, For example, Medical Image Analysis,
Analysis of images from satellites, etc.
Image Negatives:
assume the gray level range is [0, L-1]:
S = L-1-r
[Type here]
COMP4500
FACULTY OF ENGINEERING – SOHAR
UNIVERSITY
This expression results in reversing of the gray level intensities of the image
thereby producing a negative like image. The output of this function can be directly
mapped into the gray scale look-up table consisting values from 0 to L-1. Useful
for enhancing white and gray details embedded in black regions.
Log Transformations:
S = c log(1+r)
–
Where c is a constant and it is assumed that r≥0.
–
Stretch low gray levels and compress high gray level.
Power-Law Transformations:
S = c rγ
–
where c and γ are positive constants
Histogram
The second part of this experiment will deal with the histogram of a digital image.
For a gray-level image in the range [0, L-1], the histogram is a discrete function:
h(rk) = nk
Where rk is the kth gray level and nk is the number of pixels in the image having
level rk. It is a common practice to normalize the histogram by dividing each of
its values by the total number of pixels in the image, denoted by n.
Thus a normalized histogram is given by:
p(rk) = nk/n
[Type here]
COMP4500
FACULTY OF ENGINEERING – SOHAR
UNIVERSITY
Exercises :
Download the provided images from SULMS in order to perform these
exercises and make sure that the images are stored in the workspace.
Before writing the program please run the following command:
pkg load image
Exercise1: Image Negatives
1. Write the following code in a MATLAB m-file:
%img_neg.m
close all;
clear all;
I=imread(‘image_1.jpg’); I=im2double(I);
for i=1:size(I,1)
for j=1:size(I,2)
I1(i,j)=1-I(i,j);
end
end
subplot(121),imshow(I),title(‘original image’)
subplot(122),imshow(I1),title(‘enhanced image (image negative)’)
Comment on your results.
Exercise2: Power-Law Transformations
%power_tr.m
close all;
clear all;
clc;
I=imread(‘image_2.tif’);
I=im2double(I);
c=input(‘Enter the value of the constant c=’);
g=input(‘Enter the value of gamma g=’);
for i=1:size(I,1)
for j=1:size(I,2)
I3(i,j)=c*I(i,j)^g;
end
end
subplot(121), imshow(I),title(‘original image’)
subplot(122), imshow(I3),title(‘power-low transformation’)
[Type here]
COMP4500
FACULTY OF ENGINEERING – SOHAR
UNIVERSITY
2. Change the values of c and gamma (at least 3 values each), and
comment on your results.
[Type here]
COMP4500
FACULTY OF ENGINEERING – SOHAR
UNIVERSITY
Exercise3: Image Histogram
%hist_ex.m
close all;
clear all;
i=imread(‘lena_color_256.tif’);
I=rgb2gray(i);
m=im2bw(i,1); %Black
subplot (1,2,1),imhist(I),title(‘Gray Image’);
subplot (1,2,2),imhist(m),title(‘Binary Image’)
comment on your results.
Exercise4: Histogram Equalization
clc
close all
clear all
f = imread(‘image_4.tif’);
g = histeq(f);
subplot (2,2,1),imshow(f),title(‘original image’)
subplot (2,2,2),imhist(f),title(‘original Image
Histogram’),ylim(‘auto’)
subplot (2,2,3),imshow(g),title(‘Image after Histogram Equalization’)
subplot (2,2,4),imhist(g),title(‘Histogram Equalization’),ylim(‘auto’)
comment on your results.
[Type here]
COMP4500
FACULTY OF ENGINEERING – SOHAR
UNIVERSITY
Exercise5: Image enhancement using subtraction
clc;
close all;
clear all;
I=imread(‘image_5.tif’);
I1=imread(‘Image_6.tif’);
if length(size(I))>2
%length must be 2(gray)
I=rgb2gray(I);
end
if length(size(I1))>2
I1=rgb2gray(I1);
end
I2=I-I1;
I3=histeq(I2);
subplot(221),imshow(I),title(‘First Image’);
subplot(222),imshow(I1),title(‘Second Image’);
subplot(223),imshow(I2),title(‘Difference Image’);
subplot(224),imshow(I3),title(‘Enhanced Difference Image’);
comment on your results.
Exercise6: Image Enhancement using averaging
clc
close all
clear all
I=imread(‘image_7.jpg’);
if length(size(I))>2
I=rgb2gray(I);
end
I=im2double(I);
for i=1:8
N=imnoise(I,’gaussian’);
%Gaussian white noise
eval([‘In’ num2str(i) ‘ =N;’])
% eval(EXPRESSION) evaluates the MATLAB code
%in the string EXPRESSION.(In1=N);
end
Ia2=(In1+In2)/2;
Ia4=(Ia2+In3+In4)/4;
Ia6=(Ia4+In5+In6)/6;
Ia8=(Ia6+In7+In8)/8;
subplot(321),imshow(I),title(‘Original Image’);
subplot(322),imshow(N),title(‘noisy Image’);
subplot(323),imshow(Ia2),title(‘Approx. due to 2 images’)
subplot(324),imshow(Ia4),title(‘Approx. due to 4 images’)
subplot(325),imshow(Ia6),title(‘Approx. due to 6 images’)
subplot(326),imshow(Ia8),title(‘Approx. due to 8 images’)
Comment on your results.
[Type here]
COMP4500
FACULTY OF ENGINEERING – SOHAR
UNIVERSITY
Lab tasks:
1. Enhance your image jpg( black and white image) using the three
intensity transformations described above. Let c =1 always. Use different
values of γ (0.3, 0.4, 0.6, 1.0, 3.0, and 5.0) for each image.
2. Comment on the results you have got.
3. Redo excursive 3 and 4 using your image
4. Redo excursive 6 using salt and paper noise.
[Type here]
Top-quality papers guaranteed
100% original papers
We sell only unique pieces of writing completed according to your demands.
Confidential service
We use security encryption to keep your personal data protected.
Money-back guarantee
We can give your money back if something goes wrong with your order.
Enjoy the free features we offer to everyone
-
Title page
Get a free title page formatted according to the specifics of your particular style.
-
Custom formatting
Request us to use APA, MLA, Harvard, Chicago, or any other style for your essay.
-
Bibliography page
Don’t pay extra for a list of references that perfectly fits your academic needs.
-
24/7 support assistance
Ask us a question anytime you need to—we don’t charge extra for supporting you!
Calculate how much your essay costs
What we are popular for
- English 101
- History
- Business Studies
- Management
- Literature
- Composition
- Psychology
- Philosophy
- Marketing
- Economics