BABSAHEB BHIMRAO AMBEDKAR
UNIVERSITY
Digital Image Processing Lab
Practical File (ECS-851)
2023-2024
Submitted by- Submitted to-
Abdulla Ansari Dr. P.D. Saxena
208705 Faculty of CSE
B.tech(CS) 8th Sem
Page | 1
INDEX
S.no Program Date Sign
1 Write a program for image morphology
(boundary extraction, interior filling).
2 To apply Laplacian Transformation on Image
3 To Apply Low Pass Filter on the image
4 Program to Implement Edge Detection on an
image (Edge detection)
5 To display the Gray scale images
Page | 2
Program No. 1
Write a program for image morphology
(boundary extraction, interior filling).
Input:
Original Image
Code:
MATLAB
clc;
close all;
clear all;
% Read Colour Image and convert it to a grey
level Image
% Display the original Image
I= imread('image10.png');
%I rgb2gray(RGB);
I1 = imtophat(I, strel('disk', 10));
figure, imshow(I1);
I2 = imadjust(I1);
figure,imshow(I2);
Page | 3
level = graythresh(I2);
BW = im2bw(I2,level);
figure,imshow(BW);
C=~BW;
figure,imshow(C);
D = ~bwdist(C);
D(C) = -Inf;
L = watershed(D);
Wi=label2rgb(L,'hot','w');
figure,imshow(Wi);
im=I;
Call the above function using the MATLAB command window.
Output:
Page | 4
Program No. 2
To apply Laplacian Transformation on Image
Input:
Original Image
Code:
MATLAB
clc;
close all;
clear all;
% Read Colour Image and convert it to a grey
level Image
% Display the original Image
mycolourimage = imread('image1.jpg');
myimage = rgb2gray(mycolourimage);
subplot(3,3,1);
imshow(myimage); title('Original Image');
% Apply Laplacian Filter
f=fspecial('laplacian');
lapedg = imfilter(myimage,f,'symmetric');
Page | 5
subplot(3,3,7);
imshow(lapedg,[]); title('Laplacian Filter');
Call the above function using the MATLAB command window.
Output:
Page | 6
Program No. 3
To Apply Low Pass Filter on the image
Approach:
Step 1: Input – Read an image
Step 2: Saving the size of the input image in pixels
Step 3: Get the Fourier Transform of the input_image
Step 4: Assign the Cut-off Frequency
Step 5: Designing filter: Ideal Low Pass Filter
Step 6: Convolution between the Fourier Transformed input image and the filtering mask
Step 7: Take Inverse Fourier Transform of the convoluted image
Step 8: Display the resultant image as output
Implementation in MATLAB:
% MATLAB Code | Ideal Low Pass Filter
% Reading input image : input_image
input_image = imread('[name of input image
file].[file format]');
% Saving the size of the input_image in
pixels-
% M : no of rows (height of the image)
Page | 7
% N : no of columns (width of the image)
[M, N] = size(input_image);
% Getting Fourier Transform of the
input_image
% using MATLAB library function fft2 (2D
fast fourier transform)
FT_img = fft2(double(input_image));
% Assign Cut-off Frequency
D0 = 30; % one can change this value
accordingly
% Designing filter
u = 0:(M-1);
idx = find(u>M/2);
u(idx) = u(idx)-M;
v = 0:(N-1);
idy = find(v>N/2);
v(idy) = v(idy)-N;
% MATLAB library function meshgrid(v, u)
returns
% 2D grid which contains the coordinates of
vectors
% v and u. Matrix V with each row is a copy
% of v, and matrix U with each column is a
copy of u
[V, U] = meshgrid(v, u);
% Calculating Euclidean Distance
Page | 8
D = sqrt(U.^2+V.^2);
% Comparing with the cut-off frequency and
% determining the filtering mask
H = double(D <= D0);
% Convolution between the Fourier
Transformed
% image and the mask
G = H.*FT_img;
% Getting the resultant image by Inverse
Fourier Transform
% of the convoluted image using MATLAB
library function
% ifft2 (2D inverse fast fourier transform)
output_image = real(ifft2(double(G)));
% Displaying Input Image and Output Image
subplot(2, 1, 1), imshow(input_image),
subplot(2, 1, 2), imshow(output_image, [ ]);
Input Image – Output Image-
Page | 9
Program No. 4
Program to Implement Edge Detection on an image(Edge detection)
.Approach:
Step 1: Input – Read an image
Step 2: Convert the true-color RGB image to the grayscale image
Step 3: Convert the image to double
Step 4: Pre-allocate the filtered_image matrix with zeros
Step 5: Define Prewitt Operator Mask
Step 6: Edge Detection Process (Compute Gradient approximation and magnitude of vector)
Step 7: Display the filtered image
Step 8: Thresholding on the filtered image
Step 9: Display the edge-detected image
Implementation in MATLAB:
% MATLAB Code | Prewitt Operator from Scratch
% Read Input Image
input_image = imread('[name of input image file].[file format]');
% Displaying Input Image
input_image = uint8(input_image);
figure, imshow(input_image); title('Input Image');
% Convert the truecolor RGB image to the grayscale image
input_image = rgb2gray(input_image);
% Convert the image to double
input_image = double(input_image);
% Pre-allocate the filtered_image matrix with zeros
filtered_image = zeros(size(input_image));
% Prewitt Operator Mask
Mx = [-1 0 1; -1 0 1; -1 0 1];
My = [-1 -1 -1; 0 0 0; 1 1 1];
% Edge Detection Process
% When i = 1 and j = 1, then filtered_image pixel
% position will be filtered_image(2, 2)
% The mask is of 3x3, so we need to traverse
% to filtered_image(size(input_image, 1) - 2
%, size(input_image, 2) - 2)
% Thus we are not considering the borders.
for i = 1:size(input_image, 1) - 2
for j = 1:size(input_image, 2) - 2
Page | 10
% Gradient approximations
Gx = sum(sum(Mx.*input_image(i:i+2, j:j+2)));
Gy = sum(sum(My.*input_image(i:i+2, j:j+2)));
% Calculate magnitude of vector
filtered_image(i+1, j+1) = sqrt(Gx.^2 + Gy.^2);
end
end
% Displaying Filtered Image
filtered_image = uint8(filtered_image);
figure, imshow(filtered_image); title('Filtered Image');
% Define a threshold value
thresholdValue = 100; % varies between [0 255]
output_image = max(filtered_image, thresholdValue);
output_image(output_image == round(thresholdValue)) = 0;
% Displaying Output Image
output_image = im2bw(output_image);
figure, imshow(output_image); title('Edge Detected Image');
Input Image –
Page | 11
Filtered Image:
Edge Detected Image:
Advantages:
1. Good performance on detecting vertical and horizontal edges
2. Best operator to detect the orientation of an image
Limitations:
1. The magnitude of coefficient is fixed and cannot be changed
2. Diagonal direction points are not preserved always
Page | 12
Program No. 5
To display the Gray scale images
Approach :
Read the source image file into image matrix
Convert it to grayscale, if it is an RGB image
Iterate over image matrix and count the frequency of every possible value of intensity
plot the counted frequency
% Read source image file
img = imread('apple.jpg');
% Convert image to grayscale image
img=rgb2gray(img);
% get the dimension of the image
[x, y] = size(img);
% Create a frequency array of size 256
frequency = 1 : 256;
count = 0;
% Iterate over grayscale image matrix
% for every possible intensity value
% and count them
for i = 1 : 256
for j = 1 : x
for k = 1 : y
Page | 13
% if image pixel value at location (j, k)
is i-1
% then increment count
if img(j, k) == i-1
count = count + 1;
end
end
end
% update ith position of frequency array
with count
frequency(i) = count;
% reset count
count = 0;
end
n = 0 : 255;
% Display Histogram
stem(n, frequency);
grid on;
ylabel('Number of pixels with such intensity
levels -->');
xlabel('Intensity Levels -->');
title('HISTOGRAM OF THE IMAGE');
Input:
Page | 14
Output:
Page | 15
