Image Processing by point processing

This program will demonstrate various image enhancement techniques using gray level manipulation

Contents

• Start
• Read the Image
• Digital Negative
• Log Transformation
• Power Law Transformation
• Contrast Stretching
• Gray Level Slicing

Start

clear all;
close all;
clc;

Read the Image

x1 = imread('coins.png');
x = double(x1)/255;
imshow(x);

Digital Negative

In this method: f(x,y) = L-Im(x,y), where L is the largest gray level in the image

x_neg = 1-x;
figure; imshow(x_neg)

Log Transformation

In this method: f(x,y) = c*log(Im(x,y)) where c is any constant and Im > 0
for c = 0.4

c = 0.4;
x_log = c*log(1+x);
figure; imshow(abs(x_log));

For c = 3

c = 3;
x_log = c*log(1+x);
figure; imshow(abs(x_log));

Power Law Transformation

In this method: f(x,y) = c*Im(x,y)^g where c and g are positive constants
for c = 0.5, g = 2

c = 0.5;
g = 2;
x_pow = c*x.^g;
figure; imshow(abs(x_pow));

for 1.5 = 2, g = 0.5

c = 1.5;
g = 0.5;
x_pow = c*x.^g;
figure; imshow(abs(x_pow));

Contrast Stretching

This program demonstrate piecewise continuous gray level transformation here we user three regions as: 0 to a1 transformed with the slope of m1 and a2 to 255 with slope of m1

m1 = 2.5;
m3 = 0.8;
a1 = 50;
a2 = 120;
c1 = 255 - 255*m3;
m2 = (m3*a2+c1-m1*a1)/(a2-a1);
c2 = m1*a1-m2*a1;
[row col] = size(x1);
x_contrast = ones(row,col);
for i = 1:row
    for j = 1:col
        if x1(i,j) < a1
            x_contrast(i,j) = x1(i,j)*m1;
        elseif x1(i,j) > a2
            x_contrast(i,j) = x1(i,j)*m3+c1;
        else
            x_contrast(i,j) = x1(i,j)*m2;
        end
    end
end
figure;imshow(uint8(x_contrast));
y = [(0:a1-1)*m1 ((a1:a2-1))*m2+c2 (a2:255)*m3+c1];
figure; plot(y);

Gray Level Slicing

This program demonstrate the gray level slicing operation on image
Without Background:

x_GLS = ones(row,col);
a = 48;
b = 80;
c = 190;
for i = 1:row
    for j = 1:col
        if x1(i,j) < a || x1(i,j) > b
            x_GLS(i,j) = c;
        else
            x_GLS(i,j) = 0;
        end
    end
end
figure;imshow(uint8(x_GLS));
y = [(0:a1-1)*0 (ones(1,a2-a1+1))*c (a2:255)*0];
figure; plot(y);

With Background:

c = 190;
for i = 1:row
    for j = 1:col
        if x1(i,j) < a || x1(i,j) >b
            x_GLS(i,j) = c;
        else
            x_GLS(i,j) = x1(i,j);
        end
    end
end
figure;imshow(uint8(x_GLS));
y = [(0:a1-1) (ones(1,a2-a1+1))*c (a2:255)];
figure; plot(y);

Spatial Domain Filtering

This program demonstrate the image filtering in spatial domain using various types of box filters

Contents

• Start
• Choose your mask
• Read your image
• Filter the image
• Display the result

Start

clear all;
close all;
clc;

Choose your mask

hLP = ones(3,3)/9;
hLap = [0 -1 0; -1 4 -1; 0 -1 0]; %Laplacian
hPreH = [-1 -1 -1; 0 0 0; 1 1 1]; %Prewitt
hPreV = [1 0 -1; 1 0 -1; 1 0 -1]; %Prewitt
hSobV = [1 0 -1; 2 0 -2; 1 0 -1]; %Sobel
hSobH = [-1 -2 -1; 0 0 0; 1 2 1]; %Sobel
hHP = [-1 -1 -1; -1 8 -1; 1 -1 -1]; %Sharpening
hHB = [-1 -1 -1; -1 8.2 -1; 1 -1 -1]; % High boost
hUnsharp = [-1 -1 -1; -1 1 -1; -1 -1 -1]/9; %Unsharp Masking

Read your image

x = imread('coins.png');
x = double(x)/255;

Filter the image

imLP = conv2(x,hLP,'same');
imLap = conv2(x,hLap,'same');
imPreH = conv2(x,hPreH,'same');
imPreV = conv2(x,hPreV,'same');
imPre = sqrt(imPreH.^2+imPreV.^2);
imSobH = conv2(x,hSobH,'same');
imSobV = conv2(x,hSobV,'same');
imSob = sqrt(imSobH.^2+imSobV.^2);
imHP = conv2(x,hHP,'same');
imHB = conv2(x,hHB,'same');
imUnsharp = conv2(x,hUnsharp,'same');

Display the result

Original Image

imshow(x);

Low Pass Image

figure; imshow(abs(imLP));

Laplacian Image

figure; imshow(abs(imLap));

Prewitt Operated Image

figure; imshow(abs(imPre));

Sobel Operated Image

figure; imshow(abs(imSob));

High Pass Image

figure; imshow(abs(imHP));

High Boost Filtering

figure; imshow(abs(imHB));

Unsharp Masking

figure; imshow(abs(imUnsharp));

Frequency Domain Filtering

This program demonstrate the frequency domain filtering techniques like lowpass, high pass, laplacian filtering etc

Contents

• Start
• Read the Image
• Take the fourier transform
• Filter Design
• Pass the image through filter
• Display the result

Start

clear all;
close all;
clc;

Read the Image

%x1 = imread('broken.jpg');
%x1 = double(rgb2gray(x1))/255;
x1 = imread('cameraman.tif');
%x1 = imread('coins.png');
x1 = double(x1)/255;
%x1 = ones(9,9);

Take the fourier transform

F = fft2(x1);

Filter Design

[x y] = size(F);
d0 = 50;
H = ones(size(F));
for i = 1:x-1
for j =1:y-1
dist = ((i-x/2)^2 + (j-y/2)^2)^.5;
if dist > d0
H(i,j) = 0;
end
end
end

Pass the image through filter

F = fftshift(F);
Fhat = H.*F;
Fhat = fftshift(Fhat);
r = ifft2(Fhat);
r = abs(r/max(max(r)));

Display the result

Original Image

imshow(x1);

Filtered image

figure;imshow(r);