DEPARTMENT OF COMPUTER ENGINEERING &
APPLICATIONS
DIP LAB FILE
Name: Pavan Kumar Sharma
University Rollno. : 2115000702
Subject Name: Digital Image Processing Lab
Subject Code: BCSE 0101
Course: B.Tech.
Year: III Semester: V
INDEX
Exp. Page
Program
No. No.
1 Introduction with the MATLAB Command. 1
2 Read the image and display it. 1
3 Create the horizontal and vertical strips. 1
4 Create chessboard. 2
5 Read the colored image and check attributes. 2
6 Perform Log Transformation on Image. 3
7 Perform Negative Transformation on Image. 3
8 Perform Power Log Transformation on Image. 4
9 Perform Gray Level Slicing with and without background. 4-5
10 Perform Histogram Equalization on image. 5-6
11 Find Four, Diagonal and Eight Neighbours of image. 6-7
12 Perform Dilation on Image. 7-8
13 Perform Erosion on Image. 8-9
14 Perform Perforn Closing(Dilation Followed by Erosion) on Image. 9-10
Experiment -1
Objective:- Introduction with the MATLAB Command.
Commands:-
1. clc - Clear Command window
2. clearall - clear the workspace
3. date - displays current date
4. linspace - creates regularly spaced rectory
5. Mat – returns largest element
6. Min - returns smallest element
7. size – computes array size
8. ones – creates an array of ones
9. zeros – creates an array of zeros
10. subplot – creates plot in subwindows.
Experiment -2
Objective:- Read the image and display it.
Implement:-
Img = imread(’cameraman.tif’);
imshow(Img);
Output:-
Experiment -3
Objective:- Create the horizontal and vertical strips.
Implement:-
zero_arr = zeros(10,20); %m*n zeros matrix
%horizontal strips
hor_strip = uint8(zero_arr);
hor_strip(2:2:10,:) = 255;
subplot(1,2,1),imshow(hor_strip)
%vertical strip
ver_strip = uint8(zero_arr);
ver_strip(:,2:2:20) = 255; % array(st_row:diff:end_row,st_col:diff:end_col)
subplot(1,2,2),imshow(ver_strip)
Output:-
Experiment -4
Objective:- Create chessboard.
Implement:-
zero_arr = zeros(8,8); %m*n zeros matrix
%chess
hor_strip = uint8(zero_arr);
hor_strip(2:2:8,:) = 255;
hor_strip(:,2:2:8) = 255;
hor_strip(2:2:8,2:2:8) = 0;
subplot(1,2,1),imshow(hor_strip)
Output:-
Experiment -5
Objective:- Read the colored image and check attributes.
Implement:-
i1 = imread ("peppers.png");
r = i1 (:,:,1);
g = i1(:,:,2);
b = i1(:,:,3);
subplot(2,2,1);
imshow (i1);
subplot(2,2,2);
imshow (r);
subplot(2,2,3);
imshow (g);
subplot(2,2,4);
imshow (b);
Output:-
Experiment -6
Objective:- Perform Log Transformation on Image.
Implement:-
img = imread('cameraman.tif');
subplot(1,2,1), imshow(img)
% Convert datatype to Double
% (for allowing fractional values)
r = double(img);
% Constant determining the
% nature of the log curve
C = 40;
% Performing log transformation
S = C * log(1 + r);
%T = 255/(C * log(256));
% Converting the datatype back
% to integer for displaying
Log = uint8(S);
subplot(1,2,2), imshow(Log)
Output:-
Experiment -7
Objective:- Perform Negative Transformation on Image.
Implement:-
img = imread('cameraman.tif');
%Negative Transformation - 1
neg=255-img;
subplot(1,2,1),imshow(img)
%Negative Transformation - 2
neg=img;
for row=1:size(img,1)
for col=1:size(img,2)
neg(row,col,:)=255-img(row,col,:);
end
end
subplot(1,2,2),imshow(neg)
Output:-
Experiment -8
Objective:- Perform Power Log Transformation on Image.
Implement:-
img = imread('cameraman.tif');
subplot(1,2,1), imshow(img)
% Convert datatype to Double
% (for allowing fractional values)
r = double(img);
% Constant determining the
% nature of the log curve
C = 100;
gamma = 0.5;
% Performing power log transformation
S = C * log(1 + r)^gamma;
T = 255/(C * log(256)^gamma);
% Converting the datatype back
% to integer for displaying
Log = uint8(T * S);
subplot(1,2,2), imshow(Log)
Output:-
Experiment -9
Objective:- Perform Gray Level Slicing with and without background.
Implement:-
% Read the input image
grayImage = imread('cameraman.tif');
% Set the threshold values (adjust as needed)
lowThreshold = 50;
highThreshold = 150;
% Perform gray level slicing without background reference
outputImage = grayImage;
outputImage(grayImage > lowThreshold & grayImage < highThreshold) =
255;
% Perform gray level slicing with background reference
whback= grayImage;
whback(grayImage < lowThreshold | grayImage > highThreshold) = 255;
subplot(2,2,3), imshow(whback), title('With Background')
% Display the original and processed images
figure;
subplot(2, 2, 1), imshow(grayImage), title('Original Image');
subplot(2, 2, 2), imshow(outputImage), title('Gray Level Slicing
without Background Reference');
Output:-
Experiment -10
Objective:- Perform Histogram Equalization on image.
Implement:-
img=imread('cameraman.tif');
[n,m]=size(img);
%n-->row
%m-->col
% First Column --> rk
for i=1:256
t(i,1)=i-1;
t(i,2)=0;
end
% Second Column --> nk
%Frequency Count
for i=1:n
for j=1:m
pix=img(i,j)+1;
t(pix,2)=t(pix,2)+1;
end
end
for i=1:256
t(i,3)=t(i,2)/(256*256);
end
t(1,4)=t(1,3)
for i=2:256
t(i,4)=t(i-1,4)+t(i,3);
end
for i=1:256
t(i,5)=t(i,4)*255;
end
for i=1:256
t(i,6)=round(t(i,5));
end
for i=1:256s(i,1)=i-1;
s(i,2)=0;
end
for i=1:256
pix=t(i,6)+1;
s(pix,2)=s(pix,2)+t(i,2);
end
img_2=img;
for i=1:n
for j=1:m
img_2(i,j)=t(img(i,j)+1,6);
end
end
subplot(3,2,1),imshow(img),title('Original');
subplot(3,2,2),imshow(img_2),title('Manual Transform');
subplot(3,2,3),bar(t(:,1),t(:,2)),title('Histogram');
subplot(3,2,4),bar(s(:,1),s(:,2)),title('Histogram');
subplot(3,2,5),imhist(img_2),title("Equalized");
Output:-
Experiment -11
Objective:- Find Four, Diagonal and Eight Neighbours of image.
Implement:-
% Define the image matrix
img = [1, 2, 3;
4, 5, 6;
7, 8, 9];
% Input values for x and y
x = input('X-axis: ');
y = input('Y-axis: ');
% Define height and width
[height, width] = size(img);
fprintf('Height: %d, Width: %d\n', height, width);
% Initialize arrays for neighbors
fourN = NaN(1, 4);
eightN = NaN(1, 8);
diagonalN = NaN(1, 4);
% Four Neighbour
if x > 1
fourN(1) = img(x - 1, y);
end
if y > 1
fourN(2) = img(x, y - 1);
end
if x < height
fourN(3) = img(x + 1, y);
end
if y < width
fourN(4) = img(x, y + 1);
end
% Diagonal Neighbour
if x > 1 && y > 1
diagonalN(1) = img(x - 1, y - 1);
end
if x < height && y < width
diagonalN(2) = img(x + 1, y + 1);
end
if x < height && y > 1
diagonalN(3) = img(x + 1, y - 1);
end
if x > 1 && y < width
diagonalN(4) = img(x - 1, y + 1);
end
% Display the results
fprintf('Four Neighbors: %s\n', mat2str(fourN));
fprintf('Diagonal Neighbors: %s\n', mat2str(diagonalN));
% Combine eight neighbors
eightN(1:4) = fourN(1:4);
eightN(5:8) = diagonalN(1:4);
fprintf('Eight Neighbors: %s\n', mat2str(eightN));
Output:-
Experiment -12
Objective:- Perform Dilation on Image.
Implement:-
% Create a binary image
binaryImage = imread('cameraman.tif')
binaryImage = im2bw(binaryImage);
% Create a structuring element (a simple 3x3 square)
se = logical(ones(3, 3));
% Get the size of the binary image
[rows, cols] = size(binaryImage);
% Initialize the result image
dilatedBinaryImage = false(rows, cols);
% Iterate through the image and perform dilation
for r = 2:rows-1
for c = 2:cols-1
% Check if any of the structuring element pixels overlap with
the image
if any(any(binaryImage(r-1:r+1, c-1:c+1) & se))
dilatedBinaryImage(r, c) = true;
end
end
end
% Display the original and manually dilated binary images
figure;
subplot(2, 1, 1),imshow(binaryImage);
title('Original Binary Image');
subplot(2, 1, 2),imshow(dilatedBinaryImage);
title('Manually Dilated Binary Image');
Output:-
Experiment -13
Objective:- Perform Erosion on Image.
Implement:-
% Create a binary image
binaryImage = imread('cameraman.tif');
binaryImage = im2bw(binaryImage);
% Create a structuring element (a simple 3x3 square)
se = logical(ones(3, 3));
[rows, cols] = size(binaryImage);
erodedBinaryImage = true(rows, cols);
% Iterate through the image and perform erosion
for r = 2:rows-1
for c = 2:cols-1
% Check if all structuring element pixels overlap with the
image
if all(all(binaryImage(r-1:r+1, c-1:c+1) & se))
erodedBinaryImage(r, c) = true;
else
erodedBinaryImage(r, c) = false;
end
end
end
% Display the original and manually eroded binary images
figure;
subplot(1, 2, 1),imshow(binaryImage);
title('Original Binary Image');
subplot(1, 2, 2),imshow(erodedBinaryImage);
title('Manually Eroded Binary Image');
Output:-
Experiment -14
Objective:- Perform Perforn Closing(Dilation Followed by Erosion) on Image.
Implement:-
%Closing
% Create a binary image
binaryImage = imread('cameraman.tif')
binaryImage = im2bw(binaryImage);
% Create a structuring element (a simple 3x3 square)
se = logical(ones(3, 3));
% Get the size of the binary image
[rows, cols] = size(binaryImage);
% Initialize the dilatedresult image
dilatedBinaryImage = false(rows, cols);
% Display the original image
figure;
subplot(2, 2, 1), imshow(binaryImage), title('Original Image');
% Iterate through the image and perform dilation
for r = 2:rows-1
for c = 2:cols-1
% Check if any of the structuring element pixels overlap with
the image
if any(any(binaryImage(r-1:r+1, c-1:c+1) & se))
dilatedBinaryImage(r, c) = true;
end
end
end
% Display the dilated image
figure;
subplot(2, 2, 2), imshow(binaryImage), title('Dilated Image');
% Initialize the result image
erodedBinaryImage = true(rows, cols);
% Iterate through the image and perform erosion
for r = 2:rows-1
for c = 2:cols-1
% Check if all structuring element pixels overlap with the image
if all(all(dilatedBinaryImage(r-1:r+1, c-1:c+1) & se))
erodedBinaryImage(r, c) = true;
else
erodedBinaryImage(r, c) = false;
end
end
end
subplot(2, 2, 3), imshow(erodedBinaryImage), title('Erosion Binary
Image');
Output:-
