1. No category

Fundamentals of
Image Processing
Laboratory Manual
Enrollment No. _______________
Name of the student:_________________________________
Government Engineering College, Rajkot
GOVERNMENT ENGINEERING COLLEGE, RAJKOT
CERTIFICATE
This
is
to
certify
that
Mr/Miss
____________________________________Enrollment No. _____________
of B.E. (E.C.) SEM-VIII has satisfactorily completed the term work
of the subject Fundamentals of Image Processing prescribed by
Gujarat Technological University during the academic term
January-2014 to April-2014.
Date:
Signature of the faculty
SET 1 completed on ________________
SET 2 completed on _________________
Lab Manual of Fundamentals of Image Processing
Page 2
List of Experiments
Sr.
No.
1.
2.
Name of Experiment
Write program to read and display digital image using MATLAB or
SCILAB
 Become familiar with SCILAB/MATLAB Basic commands
 Read and display image in SCILAB/MATLAB
 Resize given image
 Convert given color image into gray-scale image
 Convert given color/gray-scale image into black & white image
 Draw image profile
 Seprate color image in three R G & B planes
 Create color image using R, G and B three separate planes
 Flow control and LOOP in SCILAB
 Write given 2-D data in image file
To write and execute image processing programs using point
processing method
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3.
To write and execute programs for image arithmetic operations
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4.
AND operation between two images
OR operation between two images
Calculate intersection of two images
Water Marking using EX-OR operation
NOT operation (Negative image)
To write a program for histogram calculation and equalization

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
6.
Addition of two images
Subtract one image from other image
Calculate mean value of image
Different Brightness by changing mean value
To write and execute programs for image logical operations
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5.
Obtain Negative image
Obtain Flip image
Thresholding
Contrast stretching
Standard MATLAB function
Program without using standard MATLAB functions
C Program
Use Simulink to plot histogram of colour image
To write and execute program for geometric transformation of image

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
Translation
Scaling
Rotation
Shrinking
Zooming
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7.
To understand various image noise models and to write programs for
image restoration
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8.
Write and execute programs to remove noise using spatial filters
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9.
11.
12.
Understand 1-D and 2-D convolution process
Use 3x3 Mask for low pass filter and high pass filter
Write and execute programs for image frequency domain filtering

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10.
Remove Salt and Pepper Noise
Minimize Gaussian noise
Median filter and Weiner filter
Apply FFT on given image
Perform low pass and high pass filtering in frequency domain
Apply IFFT to reconstruct image
Write a program in C and MATLAB/SCILAB for edge detection using
different edge detection mask
Write and execute program for image morphological operations
erosion and dilation.
To write and execute program for wavelet transform on given image
and perform inverse wavelet transform to reconstruct image.
Instructions to the students:

Solve exercise given at the end of each practical, write answers and execute it

Write and execute all programs either in MATLAB or SCILAB. You can cut and
paste results of image processing in soft copy of this lab manual. Write all the
programs in separate folder. Prepare your folder in your laptop/computer.

Do not use standard MATLAB functions whenever specified. Write program
yourself without using standard functions

Get signature of staff regularly after completion of practical and exercise
regularly. Show your programs when faculty asks for it.

There are two SET of experiments. SET 1 having seven experiments. Students
must complete first SET of seven experiments before 15th March 2014 and get
signature of faculties in all six experiments. SET 2 having another 5
experiments. Students should complete SET2 before 30 th April 2014.
Lab Manual of Fundamentals of Image Processing
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EXPERIMENT NO. 1
AIM: Write program to read and display digital image using MATLAB or
SCILAB
Introduction of SCILAB:
SciLab is a programming language for mathematical operations It provides
development environment for mathematical programming. It is very much
useful for matrix manipulation hence it is good choice for signal processing
and image processing applications. It is similar to MATLAB. MATLAB is
commercial software while SCILAB is open source software. It has one
thousand four hundred in-built functions and it is growing day by day.
Image and Video processing toolbox will be useful for this subject.
Students can download free SCILAB software from following website:
http://www.scilab.in
You can also create your log in on the e-Prayog website:
http://59.181.142.81 and get scilab link from there. For image processing
experiments you need to download SIVP (Speech, Image, Video Processing)
Toolbox.
Saving and Loading Programs in SCILAB:
SCILAB can be used as a prototyping environment in which we try things
out, examine the results and then adjust our programs to give better
performance. We can give SCILAB commands on command prompt. For
large number of commands, we can use SCILAB editor. We can write
sequence of commands in SCILAB editor and then save as .sce files. We can
execute .sce file to see the execution of the program.
Some commands and programs in SCILAB:
[1] Declaring matrices:
In SciLab, there is no need to declare variables; new variables are simply
introduced as they are required.
A = [1 2 3; 4 5 6; 7 8 9]
The above SCILAB command declares following matrix:
 1 2 3
A =  4 5 6
7 8 9
Keep following things in mind while declaring variables/matrices:
 No spaces
 Don’t start with a number
 Variable names are case sensitive
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[2] Populating matrix elements with zeros:
SciLab also provides a number of functions which can be used to populate a
new matrix with particular values. For example to make a matrix full of
zeroes we can use the function zeros(m, n) which creates an m*n matrix of
zeros as follows:
B = zeros(3,3)
0 0 0
B = 0 0 0
0 0 0
[2] Knowing size of matrix:
Syntax:
[rows, cols] = size(A);
This command gives size of matrix
Above command gives result: rows=3, cols=3
[3] Reading Image file
We can use following command to read image file:
myImage=imread(‘File name with path’)
If name of the image file is test.bmp and if it is in /home/chv folder above
commands can be written as:
myImage=imread(‘/home/chv/test.bmp’)
The image filename can be given as a full file path or as a file path relative to
the SciLab current directory. The current directory can be changed from the
main SciLab interface window or by cd (change directory command). The
supported file formats include ‘bmp’, ‘gif’, ‘jpg’ and ‘png’.
After giving above command image data is available in myImage variable.
You can use any variable name.
[4] Displaying image
After reading image data using above function, we can display images in
SciLab using imshow function. This function simply takes the array storing
the image values as its only parameter.
Syntax:
imshow(<variable name>)
Example:
imshow(myImage);
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[5] Knowing size of image in pixels:
Size of the image in pixels can be found out by following command:
[Rows, Cols] = size(myImage)
[6] Image resizing
Image resizing can be done by following command:
imresize(Image,{Parameters});
For example:
Consider that we read the image in variable myImage using imread function
than we can resize the image stored in this variable by following command
imresize(myImage,[256,256],’nearest’);
This command will convert image of any size into image of 256x256 using
nearest neighbor technique.
[7]Converting Color image into Grayscale image:
Color image can be converted into Grayscale image by matlab/scilab
function rgb2gray.
Example:
myGrayImage=rgb2gray(myImage)
[8]Converting Color image into Binary Image:
Color image can be converted into Binary image using function im2bw:
Example:
myBWImage=im2bw(myImage)
Where myImage is 2D image data
[9] Intensity profile along line given by two points on image
Drawing of intensity profile along single line on given image (Scan one line
along the image and draw intensity values (1-D signal)) can be done by
function improfile()
Example:
improfile(myImage,[0,150],[200,150]);
If given image is chess-board pattern, profile graph would be squarewave.
[10] Separate color image into three separate R,G,B planes
If we read given color image using imread() function, we get 3-D matrix. To
separate out R,G and B planes, we can read each plane separately as
follows:
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Example:
myImage=imread(‘RGBBar.bmp’);
RED=myImage(:,:,1);
GREEN=myImage(:,:,2);
BLUE=myImage(:,:,3);
[11] Combine three separate R,G,B planes to create color image
If we have three R,G and B planes separately and if we want to create color
image from it, we can use concatenate function:
Example:
newImage=cat(3,RED,BLUE,GREEN)
[12] Flow control in SCILAB
If statements are simply used to make decisions in SciLab
Syntax:
if <condition> then
<do some work>
else
<do some other work>
end
If we wish to perform thresholding of an image we could use following
statements in SCILAB Program:
if ImageData(r, c) > threshold then
ThresholdedImage(r, c) = 255;
else
ThresholdedImage(r, c) = 0;
end
[13] Loops in SCILAB:
There is for loop and while loop in SCILAB.
While loop
While loop repeat a piece of work as long as a condition holds true.
The while loop in SciLab uses the following syntax:
while <condition>
<perform some work repeatedly…>
end
For loop in SCILAB is very popular for image processing because it is
particularly useful for iterating through the members of a matrix.
The SCILAB for loop uses the following syntax:
Lab Manual of Fundamentals of Image Processing
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for index = <start>:<finish>
<Perform some work…>
end
Example:
Following SCILAB code iterates through each pixel of image, does
summation of pixel values, finds average pixel value which represents
average brightness of the scene.
myImage=imread(‘/home/chv/test.bmp’)
[Rows, Cols] = size(myImage)
total = 0;
for rowIndex = 1:Rows
for colIndex = 1:Cols
total = total + ImageData(rowIndex, colIndex);
end
end
average=total/(Rows*Cols)
[14] Saving two dimensional matrix in Image file
We read image, image data is available in two dimensional matrix. We
process the matrix. After processing, we need to save the processed data in
form of image. To save image data in image file, following command is used.
imwrite(ImageData, ' Testout.bmp', 'bmp');
This command writes ImageData in file “Testout.bmp”. It also supports ‘gif’,
‘jpg’ and ‘png’ file formats.
:: WORKSHEET ::
[1] Give following commands in SCILAB, observe output and write
explanation of each command
t = 0:0.01:1
y=sin(2*%pi*10)
plot(y)
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[2] Create image of size 512x512 black square using monochrome, 256
gray-level using paint or any other relevant software and save it file name
“black.bmp” Read and display image using SCILAB/MATLAB commands
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[3] Execute following program for the image created in exercise[2] and
observe the result. Write comments on the result :
imgData=imread('black.bmp');
[Rows,Cols]=size(imgData);
imshow(imgData);
for i=1:Rows
for j=1:Cols
if(i>Rows/4 && i<3*Rows/4)
if(j>Cols/4 && j<3*Cols/4)
imgData(i,j)=0;
end
end
end
end
figure;
imshow(imgData);
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Write your comments on the result after executing the program:
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[4] Write MATLAB command for the following

Read color image ‘RGBBar.bmp’ given in working directory, convert it
into Grey-scale image
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Read color image ‘RGBBar.bmp’ given in working directory, Find size
of the image, Resize it to 256x256
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EXPERIMENT NO. 2
AIM: To write and execute image processing programs using point
processing methods
 Obtain Negative image
 Obtain Flip image
 Threshold operation (Thresholding)
 Contrast stretching
Introduction:
Image enhancement can be done in two domain: [1] Spatial Domain and
[2] Transform domain.
In Spatial domain Image processing, there are two ways:
 Point processing (Single pixel is processed at a time)
 Neighborhood processing (Mask processing) Convolution of 3x3 or 5x5
or other size of mask with image
In point processing, Grey level transformation can be given by following
equation
 g(x,y)=T(f(x,y))  s=T(r)
Where, f(x,y) is original image, g(x,y) is transformed image
s = gray level of transformed image
r = gray level of original image
Threshold operation:
In threshold operation, Pixel value greater than
threshold value is made white and pixel value
less than threshold value is made black. If we
consider image having gray levels r and if grey
levels in output image is s than,
s=0
if r ≤ m
s = 255
if r > m
Where m = threshold
Lab Manual of Fundamentals of Image Processing
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MATLAB Program for Thresholding:
% Experiment No. 2 Thresholding (Extreme contrast stretching)
% Scan any document and use this method to make it clean
% Example: scan your own signature and make it clean with
thresholding
filename=input('Enter file name: ','s');
y=imread(filename);
T=input('Enter threshold value between 0 to 255: ');
if(ndims(y)==3)
y=rgb2gray(y);
end
[m,n]=size(y);
imshow(y);
for i=1:m
for j=1:n
if(y(i,j)>T)
y(i,j)=255;
else
y(i,j)=0;
end
end
end
figure;
imshow(y);
Run above program for different threshold values and find out optimum
threshold value for which you are getting better result.
Horizontal flipping:
% Experiment 2: Flip given image horizontally
% Read in an image
filename=input('Enter file name: ','s');
imagedata = imread(filename);
if(ndims(imagedata)==3)
imagedata=rgb2gray(imagedata);
end
%Determine the size of the image
[rows,cols] = size(imagedata);
%Declare a new matrix to store the newly created flipped image
FlippedImageData = zeros(rows,cols);
%Generate the flipped image
for r = 1:rows
for c = 1:cols
FlippedImageData(r,cols+1-c) = imagedata(r,c);
end
end
%Display the original image and flipped image
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subplot(2,1,1); imshow(imagedata) ;
subplot(2,1,2); imshow(FlippedImageData,[]);
%Write flipped image to a file
imwrite(mat2gray(FlippedImageData),'FlipTest.jpg','quality',99);
Negative Image:
Negative Image can be obtained by subtracting each pixel value from 255.
255
s = 255-r
s
r
255
MATLAB Program to obtain Negative Image:
% Experiment No. 2
% To obtain Negative Image
% Image Processing Lab, GEC Rajkot
clc;
close;
%Original Image
[namefile,pathname]=uigetfile({'*.bmp;*.tif;*.jpg;*.gif','IMAG
E Files (*.bmp,*.tif,*.jpg,*.gif)'},'Choose GrayScale Image');
img=imread(strcat(pathname,namefile));
if(size(img,3)==3)
img=rgb2gray(img);
end
[rows,cols]=size(img);
neg_img=zeros(rows,cols);
for r = 1:rows
for c = 1:cols
neg_img(r,c) = 255-img(r,c);
end
end
%Display original and negative images
subplot(2,1,1); imshow(img);
subplot(2,1,2); imshow(neg_img,[]);
Lab Manual of Fundamentals of Image Processing
%Calculate negative image
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Contrast Stretching:
Contrast Stretching means Darkening level below threshold value m and
whitening level above threshold value m. This technique will enhance
contrast of given image. Thresholding is example of extreme constrast
stretching.
Practically contrast stretching is done by
piecewise linear approximation of graph
shown in left by following equations:
s = x.r
s = y.r
s = z.r
0 ≤ r<a
a ≤ r<b
b ≤ r<255
Where x,y and z are different slopes
a
b
RED line shows piecewise approximation
MATLAB Program for contrast stretching:
% Experiment No. 2 Contrast stretching using three slopes
% and two threshold values a & b
clc;
clear all;
[namefile,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;*.jpe
g;*.gif','IMAGE Files
(*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)'},'Chose GrayScale
Image');
img=imread(strcat(pathname,namefile));
if(size(img,3)==3)
img=rgb2gray(img);
end
[row, col]=size(img);
newimg=zeros(row,col);
a=input('Enter threshold value a: ');
b=input('Enter threshold value b: ');
for i=1:row
for j=1:col
if(img(i,j)<=a)
newimg(i,j)=img(i,j);
end
if (img(i,j)>a && img(i,j)<=b)
newimg(i,j)=2*img(i,j);
end
if(img(i,j)>b)
newimg(i,j)=img(i,j);
end
end
end
subplot(2,1,1); imshow(uint8(img));
subplot(2,1,2); imshow(uint8(newimg));
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:: WORKSHEET ::
[1] Modify program of horizontal flipping for getting vertical flipping. Apply it
on some image
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[1] In contrast stretching program take threshold values 50 and 100, Use
slope 3 for gray levels between 0 to 50, slope 2 for gray levels between 50 to
100 and slope 1 for rest gray levels. Modify and write program again.
Execute it for some image.
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Copy and Paste original and contrast stretched images here:
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EXPERIMENT NO. 3
AIM: To write and execute programs for image arithmetic operations
Introduction:
Arithmetic operations like image addition, subtraction,
multiplication and division is possible. If there is two images I1 and I2 then
addition of image can be given by:
I(x,y) = I1(x,y) + I2(x,y)
Where I(x,y) is resultant image due to addition of two images. x and y are
coordinates of image. Image addition is pixel to pixel. Value of pixel should
not cross maximum allowed value that is 255 for 8 bit grey scale image.
When it exceeds value 255, it should be clipped to 255.
To increase overall brightness of the image, we can add some constant value
depending on brightness required. In example program we will add value 50
to the image and compare brightness of original and modified image.
To decrease brightness of image, we can subtract constant value. In example
program, value 100 is subtracted for every pixel. Care should be taken that
pixel value should not less than 0 (minus value). Subtract operation can be
used to obtain complement (negative) image in which every pixel is
subtracted from maximum value i.e. 255 for 8 bit greyscale image.
Multiplication operation can be used to mask the image for obtaining region
of interest. Black and white mask (binary image) containing 1s and 0s is
multiplied with image to get resultant image. To obtain binary image
function im2bw() can be used. Multiplication operation can also be used to
increase brightness of the image
Division operation results into floating point numbers. So data type required
is floating point numbers. It can be converted into integer data type while
storing the image. Division can be used to decrease the brightness of the
image. It can also used to detect change in image.
Execute following MATLAB program:
%Experiment No. 3 Image Arithmetic Operations
%Image Processing Lab, E.C. Department, GEC Rajkot
%Image addition is done between two similar size of image, so image resize
%function is used to make size of both image same.
%I=I1+I2
clc
close all
I1 = imread('cameraman.tif');
I2 = imread('rice.png');
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subplot(2, 2, 1);imshow(I1);title('Original image I1');
subplot(2, 2, 2);imshow(I2);title('Original image I2');
I=I1+I2; % Addition of two images
subplot(2, 2, 3);imshow(I);title('Addition of image I1+I2');
I=I1-I2; %Subtraction of two images
subplot(2, 2, 4);imshow(I);title('Subtraction of image I1-I2');
figure;
subplot(2, 2, 1);imshow(I1);title('Original image I1');
I=I1+50;
subplot(2, 2, 2);imshow(I);title('Bright Image I');
I=I1-100;
subplot(2, 2, 3);imshow(I);title('Dark Image I');
M=imread('Mask.tif');
M=im2bw(M) % Converts into binary image having 0s and 1s
I=uint8(I1).*uint8(M); %Type casting before multiplication
subplot(2, 2, 4);imshow(I);title('Masked Image I');
Result after execution of program:
Original image I1
Original image I2
Addition of image I1+I2
Subtraction of image I1-I2
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Original image I1
Bright Image I
Dark Image I
Masked Image I
Display image with different brightness:
close all;
clear all;
[filename,pathname]=uigetfile({'*.bmp;*.jpg;*.gif','Choose
Image File'});
myimage=imread(filename);
if(size(myimage,3)==3)
myimage =rgb2gray(myimage);
end
[Rows, Cols] = size(myimage)
newimage=zeros(Rows,Cols);
k=1;
while k<5,
for i = 1:Rows
for j = 1:Cols
if k==1
newimage(i,j)=myimage(i,j)-100;
end
if k==2
newimage(i,j)=myimage(i,j)-50;
end
if k==3
newimage(i,j)=myimage(i,j)+50;
end
if k==4
newimage(i,j)=myimage(i,j)+50;
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end
end
end
subplot(2,2,k); imshow(newimage,[]);
k=k+1;
end
Result:
Calculate mean value:
Mean value can be calculated by MATLAB function mean()
Alternately following code can be used to calculate mean value:
[Rows, Cols] = size(myimage)
newimage=zeros(Rows,Cols);
total = 0;
for i = 1:Rows
for j = 1:Cols
total = total + myimage(i,j);
end
end
average=total/(Rows*Cols)
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:: WORKSHEET ::
[1] In above program, use functions imadd() functions to addition,
imsubtract() for subtraction immultiply() for multiplication operations.
Use imcomplement() function to get complement of image. Write
program again using these functions in following space.
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[2] Write Program to read any image, resize it to 256x256. Apply square
mask shown in following figure so that only middle part of the image is
visible.
128
256
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[3] Write your own matlab function addbrightness() and use it to increase
brightness of given image.
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EXPERIMENT NO. 4
AIM: To write and execute programs for image logical operations
Introduction: Bitwise logical operations can be performed between pixels of
one or more than one image.
AND/NAND Logical operations can be used for following applications:
 Compute intersection of the images
 Design of filter masks
 Slicing of gray scale images
OR/NOR logical operations can be used for following applications:
 Merging of two images
XOR/XNOR operations can be used for following applications:
 To detect change in gray level in the image
 Check similarity of two images
NOT operation is used for:
 To obtain negative image
 Making some features clear
Program:
Experiment No. 4 Image Logical Operations
%Image Processing Lab, E.C. Department, GEC Rajkot
%Logical operations between two image circle and pentagon is shown
clc
close all
clc
close all
myimageA=imread('circle.jpg');
myimageA=rgb2gray(myimageA);
myimageB=imread('pentagon.jpg');
myimageB=rgb2gray(myimageB);
subplot(3,2,1)
imshow(myimageA),title('Image A ');
% Display the Original Image B
subplot(3,2,2)
imshow(myimageB),title('Image B');
% Take a complement of Image A and Display it
subplot(3,2,3)
cimageA= ~myimageA ;
imshow(cimageA), title('Complement of Image A');
% Take a Ex-OR of Image A and Image B and Display it
subplot(3,2,4)
xorimage= xor(myimageA,myimageB);
imshow(xorimage), title('Image A XOR Image B');
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% Take OR of Image A and Image B and Display it
subplot(3,2,5)
orimage= myimageA | myimageB;
imshow(orimage), title('Image A OR Image B ');
% Take AND of Image A and Image B and Display it
subplot(3,2,6)
andimage= myimageA & myimageB;
imshow(andimage), title('Image A AND Image B ');
Result after execution of program:
Image A
Image B
Complement of Image A
Image A XOR Image B
Image A OR Image B
Image A AND Image B
Watermarking using EX-OR operation:
To provide copy protection of digital audio, image and video two techniques
are used: encryption and watermarking. Encryption techniques normally
used to protect data during transmission from sender to receiver. Once data
received at receiver, it is decrypted and data is same as original which is not
protected. Watermarking techniques can compliment encryption by
embedding secret key into original data. Watermarking can be applied to
audio, image or video data.
Watermarking technique used for can be visible or non-visible. For visible
watermarking technique, watermark image is visible on original image in
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light form. In non-visible watermarking technique, watermark image is
hidden inside original image. In digital watermarking, watermarking key bits
are scattered in the image and cannot be identified. There are so many
techniques being developed for secure and robust watermarking.
Watermarking using EX-OR operation is simplest technique.
In following program, Ex-OR operation is performed between original image
data and watermarking key. To extract watermarking image, EX-OR
operation is performed again between original image and watermarking
image. In this message, message GEC RAJKOT is watermarked in the digital
image in invisible form.
Program:
% Experiment No. 4 EX-OR operation to show watermarking application
% Image Processing Lab, GEC Rajkot
clc;
close;
%Original Image
[namefile,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif','IMAGE Files
(*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)'},'Chose GrayScale Image');
img=imread(strcat(pathname,namefile));
if(size(img,3)==3)
img=rgb2gray(img);
end
subplot(2,2,1);
imshow(img)
title('Original Image');
[p q] = size(img);
key = imread('KEY_GEC_RAJKOT.bmp');
key = imresize(key,[p q]);
subplot(2,2,2); imshow(key); title('Water marking key');
w_img=bitxor(uint8(img),uint8(key));
subplot(2,2,3);
imshow(w_img);title('Watermarked image');
extracted_key=bitxor(uint8(w_img),uint8(img));
subplot(2,2,4);
imshow(double(extracted_key)); title('Extracted Key');
Result:
Lab Manual of Fundamentals of Image Processing
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Original Image
Water marking key
Watermarked image
Extracted Key
:: WORKSHEET ::
[1] Prepare any two images of size 256x256 in paint. Save it in JPEG format
256 gray levels. Perform logical NOR, NAND operations between two images.
Write program and paste your results.
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Result after execution of program:
(Copy and paste original and processed image here)
Lab Manual of Fundamentals of Image Processing
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EXPERIMENT NO. 5
AIM: To write a program for histogram calculation and equalization




Standard MATLAB function
Program without using standard MATLAB functions
C Program
Use Simulink to plot histogram of colour image
Introduction:
 Histogram is bar-graph used to profile the occurrence of each gray
level in the image
 Mathematically it is represented as h(rk)=nk
Where rk is kth grey level and nk is number of pixel having that grey
level
 Histogram can tell us whether image was scanned properly or not. It
gives us idea about tonal distribution in the image.
 Histogram equalization can be applied to improve appearance of the
image

Histogram also tells us about objects in the image. Object in an image
have similar gray levels so histogram helps us to select threshold
value for object detection.
 Histogram can be used for image segmentation.
Pseudo code:
Calculate histogram:
Loop over ROWS of the image
Loop over COLS of then image
Pixel value
hist(k)=hist(k)+1
End COLS loop
End ROWS loop
i = 1 to rows
j = 1 to cols
k=data(i,j)
Calculate sum of the hist:
Loop over gray levels
i = 0 to no. of gray levels
sum=sum+hist(i);
sum_of_hist(i)=sum;
End loop
Area of image = rows*cols
Dm=Number of gray levels in the output image
Loop over ROWS
Loop over COLS
k = data(i,j)
data(i,j) = (Dm/area)*sum_of_hist(k)
End COLS oop
End ROWS Loop
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Standard MATLAB function for histogram and histogram equalization:
[1] imhist function
It computes histogram of given image and plot
Example:
myimage=imread(‘tier.jpg’)
imhist(myimage);
[1] histeq function
It computes histogram and equalize it.
Example:
myimage = imread(‘rice.bmp’);
newimage= histeq(myimage);
Sample program using standard functions:
close all;
clear all;
[filename,pathname]=uigetfile({'*.bmp;*.jpg;*.gif','Choose
Poorly scanned Image'});
myimage=imread(filename);
if(size(myimage,3)==3)
myimage =rgb2gray(myimage);
end
imhist(myimage);
newimage= histeq(myimage);
figure;
subplot(2,2,1);imshow(myimage); title('Original image');
subplot(2,2,2);imshow(newimage); title('Histogram equalized
mage');
Sample Program in MATLAB (without using standard function):
% Experiment No. 5
%Program for calculation and equalisation of the histogram
% Image Processing Lab, EC Department, GEC Rajkot
close all;
clear all;
[filename,pathname]=uigetfile({'*.bmp;*.jpg;*.gif','Choose
Poorly scanned Image'});
data=imread(filename);
if(size(data,3)==3)
data=rgb2gray(data);
end
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subplot(2,2,1);imshow(data); title('Original image');
[rows cols]=size(data)
myhist=zeros(1,256);
% Calculation of histogram
for i=1:rows
for j=1:cols
m=double(data(i,j));
myhist(m+1)=myhist(m+1)+1;
end
end
subplot(2,2,2);bar(myhist); title('Histogram of original image');
sum=0;
%Cumulative values
for i=0:255
sum=sum+myhist(i+1);
sum_of_hist(i+1)=sum;
end
area=rows*cols;
%Dm=input('Enter no. of gray levels in output image: ');
Dm=256;
for i=1:rows
for j=1:cols
n=double(data(i,j));
data(i,j)=sum_of_hist(n+1)*Dm/area;
end
end
%Calculation of histogram for equalised image
for i=1:rows
for j=1:cols
m=double(data(i,j));
myhist(m+1)=myhist(m+1)+1;
end
end
subplot(2,2,3);bar(myhist);title('Equalised Histogram');
subplot(2,2,4);imshow(data); title('Image after histogram equalisation');
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Program output:
Original image
Histogram of original image
1500
1000
500
0
Equalised Histogram
0
100
200
300
Image after histogram equalisation
3000
2000
1000
0
0
100
200
300
Sample Program in C language:
Requirements:
 Add member function histogram() in Image class for histogram
calculation and equalisation
 Add member function print_histogram() in Image class to display
histogram of an image.
 Implement this member function in implementation program
 You can use 1010 sample image to observe results
void image::histogram(char filename[])
{
int i,j,m;
FILE *f1;
int sum_of_hist[100];
f1=fopen(filename,"rb");
int k=rows*cols;
for(i=0; i<gray_levels;i++)
{
hist[i]=0;
}
fread(data,sizeof(int),k,f1);
for(i=1; i<rows;i++)
{
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for(j=1; j<cols; j++)
{
m=data[i][j];
hist[m]=hist[m]+1;
}
}
fclose(f1);
print_histogram();
getch();
/* Histogram Equalisation*/
int sum=0;
int Dm;
for(i=0; i<gray_levels; i++)
{
sum=sum+hist[i];
sum_of_hist[i]=sum;
}
int area=k;
printf("Enter no. of gray levels in output image:");
scanf("%d", &Dm);
for(i=0;i<rows;i++)
{
for(j=0;j<cols;j++)
{
int n=data[i][j];
data[i][j]=sum_of_hist[n]*Dm/area;
}
}
for(i=0;i<rows;i++)
{
for(j=0;j<cols;j++)
{
int n=data[i][j];
hist[n]=hist[n]+1;
}
}
printf("Equalised histogram:\n");
print_histogram();
getch();
}
void image::print_histogram()
{
int i,j;
printf("\n\n");
printf(" Occurence of gray levels ---->\n");
printf("
");
for(i=0;i<=25;i++)printf("_");
printf("\n");
for(i=0; i<16; i++)
{
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printf("%2d |",i);
for(j=1; j<=hist[i];j++) printf("*");
printf("\n");
}
printf("\n\n");
}
Using Simulink to plot histogram of image:
Simulink is very much user friendly if you are convergent with image
processing operations.
 Type simulink command on the command prompt
 Click on Computer Vision System toolbox
 There are various options available in this tool box, go through all the
options one by one.
 We will use following blocks in this experiment ….
[1] Computer Vision System Toolbox > Sources>Image from file
[2] Computer Vision System Toolbox > Sinks>Video Viewer
[3] Simulink > Math Operations > Matrix Concatenate
[4] Computer Vision System Toolbox > Statistics>Histogram
[5] DSP System Toolbox > Signal Processing Sinks > Vector Scope
Arrange all blocks as per following diagram and connect them.
R
Video
Viewer
G
B
Video Vi ewer
Histogram
R
Teddy.jpg
G
B
2
Histogram1
Image From Fi le
M atrix
Concatenate
User
Vector
Scope
Histogram2
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Select Image file by clicking on image block. Set following parameters in
image block:


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Sample time = inf
Image signal = Separate color signals
Output port labels: = R|G|B
Output data type: = double
Set following parameters in histogram block:
 Lower limit of histogram: 0
 Upper limit of histogram: 1
 Number of bins: = 256
Set following parameters in matrix concatenate block:
 Number of input parameters: 3
Set following parameters in Vector scope
 Scope Properties pane, Input domain = User-defined
 Display Properties pane, clear the Frame number check box
 Display Properties pane, select the Channel legend check box
 Display Properties pane, select the Compact display check box
 Axis Properties pane, clear the Inherit sample increment from input
check box.
 Axis Properties pane, Minimum Y-limit = 0
 Axis Properties pane, Maximum Y-limit = 1
 Axis Properties pane, Y-axis label = Count
 Line Properties pane, Line markers = .|s|d
 Line Properties pane, Line colors = [1 0 0]|[0 1 0]|[0 0 1]
Open the Configuration dialog box by selecting Configuration Parameters
from the Simulation menu. Set the parameters as follows:
 Solver pane, Stop time = 0
 Solver pane, Type = Fixed-step
 Solver pane, Solver = Discrete (no continuous states)
Run simulation and see result. Change file and do experiment again.
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:: WORKSHEET ::
[1] Take your own photograph in dark area. Improve its appearance using
histogram equalization technique.
(Copy and paste your original image, its histogram, equalized image and its
histogram)
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EXPERIMENT NO. 6
AIM: To write and execute program for geometric transformation of image
Introduction: We will perform following geometric transformations on the
image in this experiment
Translation: Translation is movement of image to new position.
Mathematically translation is represented as:
x’ = x +x and y’ = y + y
In matrix form translation is represented by:
x'
1
y ' 1 =  0
 0
x 
 y 
1 
0
1
0
 x
 y
 
 1 
Scaling: Scaling means enlarging or shrinking. Mathematically scaling can
be represented by:
x’ = x × Sx and y’ = y × Sy
In matrix form scaling is represented by:
x'
S x
y ' 1 =  0
 0
0
Sy
0
0
0 
1 
 x
 y
 
 1 
Rotation: Image can be rotated by an angle , in matrix form it can be
represented as:
x’ = xcos - ysin and y’ = xsin + ycos
In matrix form rotation is represented by:
x'
 cos 
y ' 1 =  sin 
 0
 sin 
cos 
0
0
0 
1 
 x
 y
 
 1 
If  is substituted with -, this matrix rotates the image in clockwise
direction.
Shearing: Image can be distorted (sheared) either in x direction or y
direction. Shearing can be represented as:
x’ = shx × y
y’ = y
In matrix form rotation is represented by:
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 1
Xshear=  sh x
 0
0
1
0
0
0 
1 
 x
 y
 
 1 
Shearing in Y direction can be given by:
x’ = x
y’ = y × shy
1
Yshear=  0
 0
sh y
1
0
0
0 
1 
 x
 y
 
 1 
Zooming : zooming of image can be done by process called pixel replication
or interpolation. Linear interpolation or some non-linear interpolation like
cubic interpolation can be performed for better result.
Program:
%Experiment No. 4 Image Geometric transformations
%Image Processing Lab, E.C. Department, GEC Rajkot
%To rotate given image using standard Matlab function imrotate()
%To transform image using standard Matlab function imtransform()
% using shearing matrix.
clc
close all
filename=input('Enter File Name :','s');
x=imread(filename);
x=rgb2gray(x);
subplot(2,2,1); imshow(x); title('Orignial Image');
y=imrotate(x,45,'bilinear','crop');
subplot(2,2,2); imshow(y); title('Image rotated by 45 degree');
y=imrotate(x,90,'bilinear','crop');
subplot(2,2,3); imshow(y); title('Image rotated by 90 degree');
y=imrotate(x,-45,'bilinear','crop');
subplot(2,2,4); imshow(y); title('Image rotated by -45 degree');
x = imread('cameraman.tif');
tform = maketform('affine',[1 0 0; .5 1 0; 0 0 1]);
y = imtransform(x,tform);
figure;
subplot(2,2,1); imshow(x); title('Orignial Image');
subplot(2,2,2); imshow(y); title('Shear in X direction');
tform = maketform('affine',[1 0.5 0; 0 1 0; 0 0 1]);
y = imtransform(x,tform);
subplot(2,2,3); imshow(y); title('Shear in Y direction');
tform = maketform('affine',[1 0.5 0; 0.5 1 0; 0 0 1]);
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y = imtransform(x,tform);
subplot(2,2,4); imshow(y); title('Shear in X-Y direction');
Result after execution of program:
Orignial Image
Image rotated by 45 degree
Image rotated by 90 degree
Image rotated by -45 degree
Result after execution of program:
Orignial Image
Shear in Y direction
Shear in X direction
Shear in X-Y direction
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Exercise:
[1] In above program, modify matrix for geometric transformation and use
imtransform() function for modified matrix. Show the results and your
conclusions.
Program:
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Result after execution of program:
Conclusion:
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EXPERIMENT NO. 7
AIM: To understand various image noise models and to write programs for
image restoration
Introduction:
Image restoration is the process of removing or minimizing known
degradations in the given image. No imaging system gives perfect quality of
recorded images due to various reasons.
Image restoration is used to improve quality of image by various methods in
which it will try to decrease degradations & noise.
Degradation of images can occur due to many reasons. Some of them are as
under:
 Poor Image sensors
 Defects of optical lenses in camera
 Non-linearity of the electro-optical sensor;
 Graininess of the film material which is utilised to store the image
 Relative motion between an object and camera
 Wrong focus of camera
 Atmospheric turbulence in remote sensing or astronomy
 Degradation due to temperature sensitive sensors like CCD
 Poor light levels
Degradation of images causes:
 Radiometric degradations;
 Geometric distortions;
 Spatial degradations.
Model of Image Degradation/restoration Process is shown in the following
figure.
Spatial domain:
g(x,y)=h(x,y)*f(x,y) + (x,y)
Frequency domain: G(u,v)=H(u,v)F(u,v)+N(u,v)
G(x,y) is spatial domain representation of degraded image and G(u,v) is
frequency domain representation of degraded image. Image restoration
applies different restoration filters to reconstruct image to remove or
minimize degradations.
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Program:
% Experiment No. 7
% To add noise in the image and apply image restoration
% technique using Wiener filter and median filter
% EC Department, GEC Rajkot
clear all;
close all;
[filename,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif','IMAGE Files
(*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)'},'Chose Image File');
A= imread(cat(2,pathname,filename));
if(size(A,3)==3)
A=rgb2gray(A);
end
subplot(2,2,1); imshow(A);title('Original image');
% Add salt & pepper noise
B = imnoise(A,'salt & pepper', 0.02);
subplot(2,2,2); imshow(B);title('Image with salt & pepper noise');
% Remove Salt & pepper noise by median filters
K = medfilt2(B);
subplot(2,2,3); imshow(uint8(K)); title('Remove salt & pepper noise by median filter');
% Remove salt & pepper noise by Wiener filter
L = wiener2(B,[5 5]);
subplot(2,2,4); imshow(uint8(L)); title('Remove salt & pepper noise by Wiener filter');
figure;
subplot(2,2,1); imshow(A);title('Original image');
% Add gaussian noise
M = imnoise(A,'gaussian',0,0.005);
subplot(2,2,2); imshow(M); title('Image with gaussian noise');
% Remove Gaussian noise by Wiener filter
L = wiener2(M,[5 5]);
subplot(2,2,3); imshow(uint8(L));title('Remove Gaussian noise by Wiener filter');
K = medfilt2(M);
subplot(2,2,4); imshow(uint8(K)); title('Remove Gaussian noise by median filter');
Exercise:
[1] Draw conclusion from two figures in this experiment. Which filter is
better to remove salt and pepper noise ?
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[2] Explain algorithm used in Median filter with example
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[3] Write mathematical expression for arithmetic mean filter, geometric
mean filter, harmonic mean filter and contra-harmonic mean filter
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[3] What is the basic idea behind adaptive filters?
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EXPERIMENT NO. 8
AIM: Write and execute programs to remove noise using spatial filters
 Understand 1-D and 2-D convolution process
 Use 3x3 Mask for low pass filter and high pass filter
Introduction:
Spatial Filtering is sometimes also known as neighborhood processing.
Neighborhood processing is an appropriate name because you define a
center point and perform an operation (or apply a filter) to only those pixels
in predetermined neighborhood of that center point. The result of the
operation is one value, which becomes the value at the center point's
location in the modified image. Each point in the image is processed with its
neighbors. The general idea is shown below as a "sliding filter" that moves
throughout the image to calculate the value at the center location.
In spatial filtering, we perform convolution of data with filter coefficients. In
image processing, we perform convolution of 3x3 filter coefficients with 2-D
image data. In signal processing, we perform convolution of 1-D data with
set of filter coefficients.
Program for 1D convolution (Useful for 1-D Signal Processing):
clear;
clc;
x=input("Enter value of x: ")
y=input("Enter value of y: ")
n=length(x)
k=length(y)
for z=n+1:n+k-1
x(z)=0;
end
for u=k+1:n+k-1
y(u)=0;
end
for i=1:n+k-1
s(i)=0
for j=1:i
s(i)=(x(j)*y(i-j+1))+s(i)
end
end
subplot(3,1,1)
plot2d3(x)
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subplot(3,1,2)
plot2d3(y)
subplot(3,1,3)
plot2d3(s)
Take value of x: [ 1 2 3 4 5 6 7 8 9 0 10 11 12 13 14 15]
Y: [-1 1]
Observe result of convolution and write your comments:
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Program for 2D convolution:
clear;
clc;
x=input("Enter value of x in matrix form: ")
y=input("Enter value of y in matrix form: ")
[xrows,xcols]=size(x);
[yrows,ycols]=size(y);
result=zeros(xrows+yrows,xcols+ycols)
for r = 1:xrows-1
for c = 1:xcols-1
sum=0;
for a=0:yrows
for b=0:ycols
sum=sum+x(r+a,c+b)*y(a+1,b+1);
end
end
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result(r,c)=sum;
end
end
Enter following 2D matrix for x:
10 10 10 10
10 10 10 10
10 10 10 10
10 10 10 10
10 10 10 10
10 10 10 10
10 10 10 10
10 10 10 10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
Enter following 2D matrix for y:
1 1 1
0 0
0
1 1 1
Observe output values and write your comments
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Program:
% Experiment No. 8 Spatial filtering using standard MATLAB function
% To apply spatial filters on given image
% EC Department, GEC Rajkot
clc;
close all;
clear all;
%Define spatial filter masks
L1=[1 1 1;1 1 1;1 1 1];
L2=[0 1 0;1 2 1;0 1 0];
L3=[1 2 1;2 4 2;1 2 1];
H1=[-1 -1 -1;-1 9 -1;-1 -1 -1];
H2=[0 -1 0;-1 5 -1;-0 -1 0];
H3=[1 -2 1;-2 5 -2;1 -2 1];
% Read the test image and display it
[filename,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif','IMAGE Files
(*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)'},'Chose Image File');
myimage = imread(cat(2,pathname,filename));
if(size(myimage,3)==3)
myimage=rgb2gray(myimage);
end
subplot(3,2,1);
imshow(myimage); title('Original Image');
L1 = L1/sum(L1);
filt_image= conv2(double(myimage),double(L1));
subplot(3,2,2);
imshow(filt_image,[]);
title('Filtered image with mask L1');
L2 = L2/sum(L2);
filt_image= conv2(double(myimage),double(L2));
subplot(3,2,3);
imshow(filt_image,[]);
title('Filtered image with mask L2');
L3 = L3/sum(L3);
filt_image= conv2(double(myimage),double(L3));
subplot(3,2,4);
imshow(filt_image,[]);
title('Filtered image with mask L3');
filt_image= conv2(double(myimage),H1);
subplot(3,2,5);
imshow(filt_image,[]);
title('Filtered image with mask H1');
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filt_image= conv2(double(myimage),H2);
subplot(3,2,6);
imshow(filt_image,[]);
title('Filtered image with mask H1');
figure;
subplot(2,2,1);
imshow(myimage); title('Original Image');
% The command fspecial() is used to create mask
% The command imfilter() is used to apply the gaussian filter mask to the image
% Create a Gaussian low pass filter of size 3
gaussmask = fspecial('gaussian',3);
filtimg = imfilter(myimage,gaussmask);
subplot(2,2,2);
imshow(filtimg,[]),title('Output of Gaussian filter 3 X 3');
% Generate a lowpass filter of size 7 X 7
% The command conv2 is used the apply the filter
% This is another way of using the filter
avgfilt = [ 1 1 1 1 1 1 1;
1 1 1 1 1 1 1;
1 1 1 1 1 1 1;
1 1 1 1 1 1 1;
1 1 1 1 1 1 1;
1 1 1 1 1 1 1;
1 1 1 1 1 1 1];
avgfiltmask = avgfilt/sum(avgfilt);
convimage= conv2(double(myimage),double(avgfiltmask));
subplot(2,2,3);
imshow(convimage,[]);
title('Average filter with conv2()');
filt_image= conv2(double(myimage),H3);
subplot(3,2,6);
imshow(filt_image,[]);
title('Filtered image with mask H3');
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:: WORKSHEET ::
[1] Write mathematical expression of spatial filtering of image f(x,y) of size
MN using mask W of size ab.
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[2] What is need for padding? What is zero padding? Why it is required?
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[3] What is the effect of increasing size of mask?
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E.C. Department, Government Engineering College, Rajkot
Page 49
EXPERIMENT NO. 9
AIM: Write and execute programs for image frequency domain filtering
Introduction:
In spatial domain, we perform convolution of filter mask with image data. In
frequency domain we perform multiplication of Fourier transform of image
data with filter transfer function.
Fourier transform of image f(x,y) of size MxN can be given by:
Where, u = 0,1,2 ……. M-1 and v = 0,1,2……N-1
Inverse Fourier transform is given by:
Where, x = 0,1,2 ……. M-1 and y = 0,1,2……N-1
Basic steps for filtering in frequency domain:






Pre-processing: Multiply input image f(x,y) by (-1)x+y to center the
transform
Computer Discrete Fourier Transform F(u,v) of input image f(x,y)
Multiply F(u,v) by filter function H(u,v) Result: H(u,v)F(u,v)
Computer inverse DFT of the result
Obtain real part of the result
Post-Processing: Multiply the result by (-1)x+y
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Program:
% Experiment 9 Program for frequency domain filtering
% EC Department, GEC Rajkot
clc;
close all;
clear all;
% Read the image, resize it to 256 x 256
% Convert it to grey image and display it
[filename,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif','IMAGE Files
(*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)'},'Chose Image File');
myimg=imread(cat(2,pathname,filename));
if(size(myimg,3)==3)
myimg=rgb2gray(myimg);
end
myimg = imresize(myimg,[256 256]);
myimg=double(myimg);
subplot(2,2,1);
imshow(myimg,[]),title('Original Image');
[M,N] = size(myimg); % Find size
%Preprocessing of the image
for x=1:M
for y=1:N
myimg1(x,y)=myimg(x,y)*((-1)^(x+y));
end
end
% Find FFT of the image
myfftimage = fft2(myimg1);
subplot(2,2,2);
imshow(myfftimage,[]); title('FFT Image');
% Define cut off frequency
low = 30;
band1 = 20;
band2 = 50;
%Define Filter Mask
mylowpassmask = ones(M,N);
mybandpassmask = ones(M,N);
% Generate values for ifilter pass mask
for u = 1:M
for v = 1:N
tmp = ((u-(M/2))^2 +(v-(N/2))^2)^0.5;
if tmp > low
mylowpassmask(u,v) = 0;
end
if tmp > band2 || tmp < band1;
mybandpassmask(u,v) = 0;
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end
end
end
% Apply the filter H to the FFT of the Image
resimage1 = myfftimage.*mylowpassmask;
resimage3 = myfftimage.*mybandpassmask;
% Apply the Inverse FFT to the filtered image
% Display the low pass filtered image
r1 = abs(ifft2(resimage1));
subplot(2,2,3);
imshow(r1,[]),title('Low Pass filtered image');
% Display the band pass filtered image
r3 = abs(ifft2(resimage3));
subplot(2,2,4);
imshow(r3,[]),title('Band Pass filtered image');
figure;
subplot(2,1,1);imshow(mylowpassmask);
subplot(2,1,2);imshow(mybandpassmask);
:: WORKSHEET ::
[1] Instead of following pre-processing step in above program use fftshift function to shift
FFT in the center. See changes in the result and write conclusion.
%Preprocessing of the image
for x=1:M
for y=1:N
myimg1(x,y)=myimg(x,y)*((-1)^(x+y));
end
end
Remove above step and use following commands.
myfftimage = fft2(myimg);
myfftimage=fftshift(myfftimage);
Conclusion:
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[2] Write a routine for high pass filter mask
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[2] Write a routine for high pass filter mask
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E.C. Department, Government Engineering College, Rajkot
Page 53
EXPERIMENT NO. 10
AIM: Write a program in C and MATLAB/SCILAB for edge detection using
different edge detection mask.
Introduction:
Image segmentation is to subdivide an image into its component regions or
objects. Segmentation should stop when the objects of interest in an
application have been isolated. Basic purpose of segmentation is to partition
an image into meaningful regions for particular application The
segmentation is based on measurements taken from the image and might be
grey level, colour, texture, depth or motion.
There are basically two types of image segmentation approaches:
[1] Discontinuity based: Identification of isolated points, lines or edges
[2] Similarity based: Group pixels which has similar characteristics by
thresholding, region growing, region splitting and merging
Edge detection is discontinuity based image segmentation approach. Edges
play a very important role in many image processing applications. It
provides outline of an object. In physical plane, edges are corresponding to
changes in material properties, intensity variations, discontinuity in depth.
Pixels on the edges are called edge points. Edge detection techniques
basically try to find out grey level transitions.
Edge detection can be done by first order derivative and second order
derivative operators.
First order line detection 3x3 mask are:
Lab Manual of Fundamentals of Image Processing
Page 54
Popular edge detection masks:
 Sobel operator performs well for image with noise compared to Prewitt
operator because Sobel operator performs averaging along with edge
detection.
 Because Sobel operator gives smoothing effect, spurious edges will not
be detected by it.
Second derivative operators are sensitive to the noise present in the image
so it is not directly used to detect edge but it can be used to extract
secondary information like …
 Used to find whether point is on darker side or white side depending
on sign of the result
 Zero crossing can be used to identify exact location of edge whenever
there is gradual transitions in the image
MATLAB Code using standard function:
% Experiment 10
% Program for edge detection using standard masks
% Image Processing Lab, EC Department, GEC Rajkot
clear all;
[filename,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif','IMAGE Files
(*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)'},'Choose Image');
A=imread(filename);
if(size(A,3)==3)
A=rgb2gray(A);
end
imshow(A);
figure;
BW = edge(A,'prewitt');
subplot(3,2,1); imshow(BW);title('Edge detection with prewitt mask');
BW = edge(A,'canny');
subplot(3,2,2); imshow(BW);;title('Edge detection with canny mask');
BW = edge(A,'sobel');
subplot(3,2,3); imshow(BW);;title('Edge detection with sobel mask');
BW = edge(A,'roberts');
subplot(3,2,4); imshow(BW);;title('Edge detection with roberts mask');
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BW = edge(A,'log');
subplot(3,2,5); imshow(BW);;title('Edge detection with log ');
BW = edge(A,'zerocross');
subplot(3,2,6); imshow(BW);;title('Edge detection with zerocorss');
MATLAB Code for edge detection using convolution in spatial domain
% EC Department, GEC Rajkot
% Experiment 9
% Program to demonstrate various point and edge detection mask
% Image Processing Lab, EC Department, GEC Rajkot
clear all;
clc;
while 1
K = menu('Choose mask','Select Image File','Point Detect','Horizontal line detect','Vertical
line detect','+45 Detect','-45 Detect','ractangle Detect','exit')
M=[-1 0 -1; 0 4 0; -1 0 -1;] % Default mask
switch K
case 1,
[namefile,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif','IMAGE Files
(*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)'},'Chose GrayScale Image');
data=imread(strcat(pathname,namefile));
%data=rgb2gray(data);
imshow(data);
case 2,
M=[-1 -1 -1;-1 8 -1;-1 -1 -1]; % Mask for point detection
case 3,
M=[-1 -1 -1; 2 2 2; -1 -1 -1]; % Mask for horizontal edges
case 4,
M=[-1 2 -1; -1 2 -1; -1 2 -1]; % Mask for vertical edges
case 5,
M=[-1 -1 2; -1 2 -1; 2 -1 -1]; % Mask for 45 degree diagonal line
case 6,
M=[2 -1 -1;-1 2 -1; -1 -1 2]; % Mask for -45 degree diagonal line
case 7,
M=[-1 -1 -1;-1 8 -1;-1 -1 -1];
case 8,
break;
otherwise,
msgbox('Select proper mask');
end
outimage=conv2(double(data),double(M));
figure;
imshow(outimage);
end
close all
%Write an image to a file
Lab Manual of Fundamentals of Image Processing
Page 56
imwrite(mat2gray(outimage),'outimage.jpg','quality',99);
:: WORKSHEET ::
[1] Get mask for “Prewitt”, “Canny”, “Sobel” from the literature and write
MATLAB/SCILAB program for edge detection using 2D convolution
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E.C. Department, Government Engineering College, Rajkot
Page 57
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[2] Understand following C++ program written for edge detection. Different
member functions are written for image class. Image class and member
function are written in Image.h header file and used in main program in
file image.cpp
//file image.h
# include <iostream.h>
# include <conio.h>
# include <stdio.h>
# include <string.h>
# define rows 10
# define cols 10
class image
{
private:
int data[10][10];
public:
void write_image(char[]);
void read_image(char[]);
void edge_detect(char[]);
};
void image::write_image(char filename[])
{
FILE *f1;
int i,j;
int k=rows*cols;
f1=fopen(filename,"wb");
for(i=0; i<rows;i++)
{
for(j=0; j<cols; j++)
{
Lab Manual of Fundamentals of Image Processing
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cin>>data[i][j];
}
}
fwrite(data,sizeof(int),k,f1);
fclose(f1);
}
void image::read_image(char filename[])
{
FILE *f1;
int i,j;
f1=fopen(filename,"rb");
int k=rows*cols;
fread(data,sizeof(int),k,f1);
for(i=0; i<rows;i++)
{
for(j=0; j<cols; j++)
{
cout<<data[i][j]<<" ";
}
cout<<endl;
}
fclose(f1);
}
void image::edge_detect(char *filename)
{
int i,j,a,b,sum,diff,maxdiff,outimage[rows][cols];
int threshold=8,max=15, min=0;
short QM[3][3]={{-1,0,-1},{0,4,0},{-1,0,-1}};
long m;
FILE *f1;
f1=fopen(filename,"rb");
int k=rows*cols;
for(i=0; i<rows;i++)
{
for(j=0; j<cols; j++) outimage[i][j]=0;
}
fread(data,sizeof(int),k,f1);
for(i=1; i<rows-1;i++)
{
for(j=1; j<cols-1; j++)
{
sum=0;
for(a=-1; a<2; a++)
{
for(b=-1;b<2;b++) sum=sum+data[i+a][j+b]*QM[a+1][b+1];
}
if(sum<0) sum=0;
if(sum>16) sum=15;
outimage[i][j]=sum;
}
}
cout<<"\nEdge detection using Quick mask:"<<endl;
for(i=0; i<rows;i++)
{
for(j=0; j<cols; j++) printf("%02d ", outimage[i][j]);
cout<<endl;
}
fclose(f1);
E.C. Department, Government Engineering College, Rajkot
Page 59
}
//file Image.cpp
# include "d:\image\image.h"
void help();
void main(int argc, char* argv[])
{
image I;
char file_name[30];
clrscr();
if(argv[1])
{
if(!argv[2])
{
printf("Enter file name: ");
scanf("%s",file_name);
}
else
{
strcpy(file_name,argv[2]);
}
if(!strcmp(argv[1],"write"))
{I.write_image(file_name);}
else if(!strcmp(argv[1],"read"))
{I.read_image(file_name);}
else if(!strcmp(argv[1],"edge"))
{ I.edge_detect(file_name); }
else if(!strcmp(argv[1],"hist"))
{ }
else
{help();}
}
else {help();}
}
void help()
{
printf("\n\t\t Image Processing Tutorial !");
printf("\t\t\tUsage :\n");
printf("\t\t\timage <operation> <filename>\n");
printf("\t\t\tFor example :\n ");
printf("\t\t\timage write data : To write image in \"data\" file\n");
printf("\n\t\t\tOther Operations:\n");
printf("\n\t\t read: read image\n");
printf("\n\t\t edge : Edge detection\n");
printf("\n\t\t hist : display histogram of image\n");
}
Lab Manual of Fundamentals of Image Processing
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Output:
Original Image of size 1010:
Edge detection after applying quick mask:
1111111111
00 00 00 00 00 00 00 00 00 00
1111111111
00 00 00 00 00 00 00 00 00 00
1111111111
00 00 00 00 00 00 00 00 00 00
1119999111
1119999111
00 00 00 15 16 16 15 00 00 00
1119999111
00 00 00 16 00 00 16 00 00 00
1119999111
00 00 00 16 00 00 16 00 00 00
1111111111
1111111111
1111111111
Exercise:
[1] Modify above program for edge detection using kirsch, prewitt & sobel mask
[2] Add member function LPF() to image class for spatial filtering of given image
Hint: Use following low pass convolution mask for low pass filtering
1/6 *
0 1 0
1 2 1
0 1 0
E.C. Department, Government Engineering College, Rajkot
Page 61
EXPERIMENT NO. 11
AIM: Write and execute program for image morphological operations:
erosion and dilation
Introduction:
 Morphology is a branch of biology that deals with form and structure
of animal and plant
 In image processing, we use mathematical morphology to extract
image components which are useful in representation and description
of region shape such as … Boundaries, Skeletons, Convex hull,
Thinning, Pruning etc.
 Two Fundamental morphological operations are: Erosion and dilation
 Dilation adds pixels to the boundaries of objects in an image, while
erosion removes pixels on object boundaries.
Erosion and dilation are two fundamental image morphological operations.
 Dilation adds pixels to the boundaries of objects in an image, while
erosion removes pixels on object boundaries.
 Dilation operation: The value of the output pixel is the maximum
value of all the pixels in the input pixel’s neighborhood. In a binary
image, if any of the pixels is set to the value 1, the output pixel is set
to 1.
 Erosion operation: The value of the output pixel is the minimum value
of all the pixels in the input pixel’s neighborhood. In a binary image, if
any of the pixels is set to 0, the output pixel is set to 0.
 Opening and closing operations can be done by combination of
erosion and dilation in different sequence.
Program:
% Program to demonstrate Erosion and Dilation
% Image Processing Lab, EC Department, GEC Rajkot
clear all;
clc;
while 1
K = menu('Erosion and dilation demo','Choose
Image','Choose 3x3 Mask','Choose 5x5 Mask','Choose
Structure Image','Erosion','Dilation','Opening',
'Closing','EXIT')
%B=[1 1 1;1 1 1;1 1 1;];
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B = strel('disk', 9);
%B = strel('disk', 5);
switch K
case 1,
[namefile,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;
*.jpeg;*.gif','IMAGE Files (*.bmp,*.tif,*.tiff,*.jpg,
*.jpeg,*.gif)'},'Chose GrayScale Image');
A=imread(strcat(pathname,namefile));
%data=rgb2gray(data);
imshow(A);
case 2,
B=[1 1 1;1 1 1;1 1 1;];
case 3,
B=[1 1 1 1 1;1 1 1 1 1;1 1 1 1 1;1 1 1 1 1;1 1 1 1 1;];
case 4,
[namefile,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;
*.jpeg;*.gif','IMAGE Files (*.bmp,*.tif,*.tiff,*.jpg,
*.jpeg,*.gif)'},'Chose GrayScale Image');
B=imread(strcat(pathname,namefile));
%data=rgb2gray(data);
figure;
imshow(B);
case 5,
C=imerode(A,B);
figure;
imshow(C);
case 6,
C=imdilate(A,B);
figure;
imshow(C)
case 7,
C=imdilate(A,B);
D=imerode(C,B);
figure;
imshow(D)
case 8,
C=imerode(A,B);
D=imdilate(C,B);
figure;
imshow(D)
case 9,
break;
otherwise,
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msgbox('Select proper mask');
end
end
close all
%Write an image to a file
imwrite(mat2gray(outimage),'outimage.jpg','quality',99);
WORKSHEET ::
[1] What is cryptography?
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------[2] What is sateganography?
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------[3] How watermarking differs from cryptography and sateganography?
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Execute above program given in this experiment on suitable image, cut and
paste resultant images on separate page and write your conclusion.
Lab Manual of Fundamentals of Image Processing
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EXPERIMENT NO. 12
AIM:
To write and execute program for wavelet transform on given image and
perform inverse wavelet transform to reconstruct image.
Introduction:
Wavelet transform is relatively recent signal and image processing tool
which has many applications. Basis functions of Fourier transform is
sinusoids while basis functions of wavelet transform is wavelets.
 Wavelets are oscillatory functions vanishing outside the small interval
hence, they are called wavelets. wave-let (small fraction of wave).
 Wavelets are building blocks of the signal.
 Wavelets are the functions which are well suited to the expansion of
real time non-stationary signals.
 Wavelets can be used to de-correlate the correlations present in real
time signal such as speech/audio, video, biomedical, seismic etc.
General block diagram of two dimensional wavelet transform:
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Following standard matlab functions are used in this experiment.
[1] wavedec2 : Function for multi-level decomposition of 2D data using
wavelet transform.
[C,S] = WAVEDEC2(X,N,'wname') returns the wavelet decomposition of the
matrix X at level N, using the wavelet named in string 'wname' (wavelet
name can be Daubechies wavelet db1, db2, db3 or any other wavelet).
Outputs are the decomposition vector C and the corresponding bookkeeping
matrix S. N is level of decomposition which must be positive integer.
[2] appcoef2: This function utilize tree developed by wavelet decomposition
function and constructs approximate coefficients for 2D data.
A = APPCOEF2(C,S,'wname',N) computes the approximation coefficients at
level N using the wavelet decomposition structure [C,S] which is generated
by function wavedec2.
[3] detcoef2: This function utilize tree develet by wavelet decomposition
function and generates detail coefficients for 2D data.
D = DETCOEF2(O,C,S,N) extracts from the wavelet decomposition structure
[C,S], the horizontal, vertical or diagonal detail coefficients for O = 'h' or 'v'
or 'd', for horizontal, vertical and diagonal detail coefficients respectively.
[4] waverec2: Multilevel wavelet 2D reconstruction (Inverse wavelet
transform)
WAVEREC2 performs a multilevel 2-D wavelet reconstruction using wavelet
named in string 'wname' (wavelet name can be Daubechies wavelet db1,
db2, db3 or any other wavelet).
X = WAVEREC2(C,S,'wname') reconstructs the matrix X based on the multilevel wavelet decomposition structure [C,S] which is generated by wavedec2
Lab Manual of Fundamentals of Image Processing
Page 66
Program:
clc;
close;
[namefile,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif','IMAGE Files
(*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)'},'Chose GrayScale Image');
X=imread(strcat(pathname,namefile));
if(size(X,3)==3)
X=rgb2gray(X);
end
imshow(X);
% Perform wavelet decomposition at level 2.
[c,s] = wavedec2(X,2,'db1');
figure;
imshow(c,[]); title('Wavelet decomposition data generated by wavedec2');
figure;
%Calculate first level approx. and detail components
ca1 = appcoef2(c,s,'db1',1);
subplot(2,2,1);imshow(ca1,[]);title('First level approx');
ch1 = detcoef2('h',c,s,1);
subplot(2,2,2);imshow(ch1,[]);title('First level horixontal detail');
cv1 = detcoef2('v',c,s,1);
subplot(2,2,3);imshow(cv1,[]);title('First level vertical detail');
cd1 = detcoef2('d',c,s,1);
subplot(2,2,4);imshow(cd1,[]);title('First level diagonal detail');
%Calculate second level approx. and detail components
figure;
ca2 = appcoef2(c,s,'db1',2);
subplot(2,2,1);imshow(ca2,[]);title('Second level approx');
ch2 = detcoef2('h',c,s,2);
subplot(2,2,2);imshow(ch2,[]);title('Second level horizontal detail');
cv2 = detcoef2('v',c,s,2);
subplot(2,2,3);imshow(cv2,[]);title('Second level vertical detail');
cd2 = detcoef2('d',c,s,2);
subplot(2,2,4);imshow(cd2,[]);title('Second level diagonal detail');
figure;
a0 = waverec2(c,s,'db1');
imshow(a0,[]);title('Reconstructed image using inverse wavelet transform');
Execute program given in this experiment on suitable image, cut and paste
resultant images on separate page and write your conclusion.
E.C. Department, Government Engineering College, Rajkot
Page 67
:: WORKSHEET ::
[1] What is wavelet ?
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------[2] How wavelet transform used to remove noise from the image?
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------[3] Write analysis low pass and high pass wavelet filter coefficients for
Daubechies-2,4 and 8 wavelets.
(Hint: use matlab function wfilters() to find filter coefficients)
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Lab Manual of Fundamentals of Image Processing
Page 68